{"as_of":"2026-08-07T22:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:74ad11400ff78c4c4307da82ef3b2e67a51a74c3f4723a44044dc7f8ca904114","coverage":[{"denominator":135,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:32:58.705589Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.23857/citation-record","integrity":"/paper/2506.23857/integrity","json":"/paper/2506.23857/citation-record.json","paper":"/paper/2506.23857"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:56.131171Z","title":"The human body at cellular resolution: the NIH Human Biomolecular Atlas Program,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.131171Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:71ae9508e480824b3e3f13a1e9bc7ae143c53bf481bb1949a3bae0910f942859","observation_id":"819e9fd4-0395-4ad2-bfc0-ccf152ba6741","resolution":{"observed_at":"2026-08-06T21:32:56.131171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/d41586-024-03498-9","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:32.554775Z","title":"Tumour atlases enable researchers to navigate through cancers,","venue":null,"work_id":"b388b9d3-a7c0-441a-9896-d93531a8bb35","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.172021Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:504d4c5db38f6c3e04c192f5a060a8094453887dcbba36ae903c62301fa8b254","observation_id":"10218697-97c3-47d8-91cd-77c1eca5970a","resolution":{"observed_at":"2026-08-06T21:33:32.623375Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s43587-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:32.299655Z","title":"Spatial mapping of cellular senescence: emerging challenges and opportunities,","venue":null,"work_id":"ad800f73-cc6d-419b-9b4a-db3c609e444f","year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.262078Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:82d1efe42f2f76db870da6e056af504c7cee7479920a45c0ccb97477f55e1f74","observation_id":"e891e5bf-48ae-4fe5-a45e-2dd020352055","resolution":{"observed_at":"2026-08-06T21:33:32.409085Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/nar/gkad782","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:32.026637Z","title":"CROST: a comprehensive repository of spatial transcriptomics,","venue":null,"work_id":"ee93cc45-03e3-46b1-8cd2-736b0af31e3e","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.367633Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:8d8b2e4ab62ae638959efe11c3ab2f0d5690efb77ce1e1cc5a1fdb5504b2f06d","observation_id":"e75e6484-d0e1-44ce-82ff-ec7a43f7e75a","resolution":{"observed_at":"2026-08-06T21:33:32.166558Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/nar/gkad933","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:31.833934Z","title":"STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization,","venue":null,"work_id":"54485eb5-77f4-4a02-bfc7-aca2b270fb99","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.468424Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:813fa73fe410124940a6943ac61cf02f642413ee26c08b1242dcae136c82dae4","observation_id":"9682f043-5556-44ac-a3e5-7dc915f86be7","resolution":{"observed_at":"2026-08-06T21:33:31.907977Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04079","last_updated":"2024-06-11T17:46:38Z","snapshot_observed_at":"2026-07-06T17:12:56.275051Z","submitted_at":"2024-01-08T18:31:38Z","title":"RudolfV: A Foundation Model by Pathologists for Pathologists","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04079","snapshot_observed_at":"2026-08-06T21:32:56.533199Z","title":"RudolfV: A Foundation Model by Pathologists for Pathologists,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.533199Z"},"links":{"cited_paper":"/paper/2401.04079","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:e182d7281d3a3f08dcab4d56cdde1706ce47b668776421d369ab99b92328d95a","observation_id":"845ebfd1-48f0-40ec-91af-e48c25d837c0","resolution":{"observed_at":"2026-08-06T21:32:56.533199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-06T21:32:56.612513Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.612513Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:69ad0d8738a1b4a6947786dff34e4f1f70287e5329526047c21ef4099d4f03ec","observation_id":"8d7d30b3-947f-4db5-bf34-bc628837b2f9","resolution":{"observed_at":"2026-08-06T21:32:56.612513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-06T05:58:29.182448Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-06T21:32:56.672037Z","title":"DINOv2: Learning Robust Visual Features without Supervision,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.672037Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:0e17998a0791743148d4b2cb9d7226bf5f4c78df31bc6e5f637edc933f0da2d7","observation_id":"27fe3b0c-5f18-4435-9a20-7715ef82ff40","resolution":{"observed_at":"2026-08-06T21:32:56.672037Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07778","last_updated":"2026-05-25T01:18:26Z","snapshot_observed_at":"2026-08-07T03:07:34.113426Z","submitted_at":"2023-09-14T15:09:35Z","title":"Virchow: A Million-Slide Digital Pathology Foundation Model","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.07778","snapshot_observed_at":"2026-08-06T21:32:56.718043Z","title":"Virchow: A Million-Slide Digital Pathology Foundation Model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.718043Z"},"links":{"cited_paper":"/paper/2309.07778","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:63a044a579524d24a9192f48b6b0ca975d8951be7d6c42b9014cb64a0d4f4188","observation_id":"10e8b98c-baa1-4f6d-b0e8-0069d05c053d","resolution":{"observed_at":"2026-08-06T21:32:56.718043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:56.765740Z","title":"Towards a general-purpose foundation model for computational pathology,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.765740Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:9551d8de0226e4e2e7bdf079d73b3c6d78a062739e1260b70c981238a2684cae","observation_id":"7590998b-b6e9-4fcb-8534-cddea5cdd21d","resolution":{"observed_at":"2026-08-06T21:32:56.765740Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.18449","last_updated":"2025-04-14T09:55:21Z","snapshot_observed_at":"2026-08-06T10:47:20.116619Z","submitted_at":"2024-07-26T01:12:54Z","title":"Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.18449","snapshot_observed_at":"2026-08-06T21:32:56.811142Z","title":"Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.811142Z"},"links":{"cited_paper":"/paper/2407.18449","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:65048bf25d7a38fedff94a08ca900da5ee89d25f0bedcf4a834be790097768e8","observation_id":"d443be83-406b-49ec-8052-b02a05f65205","resolution":{"observed_at":"2026-08-06T21:32:56.811142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:56.850565Z","title":"From whole-slide image to biomarker prediction: end-to- end weakly supervised deep learning in computational pathology,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.850565Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:740df7adbdb92fe2371a849efad21b9f35487502b6ae812d2c5d2a35002baa4a","observation_id":"bb334c9e-6b1d-44e9-8d4a-bf9ac1cd9cdf","resolution":{"observed_at":"2026-08-06T21:32:56.850565Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-06T21:32:56.890765Z","title":"Language Models are Few-Shot Learners,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.890765Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:849faf13a546aa33b5e61ee9006c5f07fdb35e70ee134924e2e5e3bf4a4aa7d1","observation_id":"5321f1d1-f6b9-46da-97ae-371af4bc8cac","resolution":{"observed_at":"2026-08-06T21:32:56.890765Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-06T21:32:56.950061Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.950061Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:69015e6063ee8ba2ad19f9fdc2a3dc80d51ca2fb7a2d61d7eb5f7d8ef83d9fc6","observation_id":"3a94b43d-1769-4a64-89cd-6795dc7aef54","resolution":{"observed_at":"2026-08-06T21:32:56.950061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-06T21:32:56.996749Z","title":"LLaMA: Open and Efficient Foundation Language Models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:56.996749Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:85c5fa732895244dcfc74fd077afe6e325aeb2fd26c28ae481ad51dfbc657ece","observation_id":"95a6ef18-c704-4846-9727-9fac35df2812","resolution":{"observed_at":"2026-08-06T21:32:56.996749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:57.063194Z","title":"A visual-language foundation model for computational pathology,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.063194Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:e1c2c8bd42fc7446f78989afe5476a52b16981eb8b2c68fd6e46a16158cda620","observation_id":"f1481f6f-80a7-43d5-9228-b75a4390ac3f","resolution":{"observed_at":"2026-08-06T21:32:57.063194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:57.124397Z","title":"A multimodal generative AI copilot for human pathology,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.124397Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:ac5983b665c2710dec5773d7466ac112d2eb9ba3964a7968e01d354fb75f7032","observation_id":"b1200d20-0499-40d6-af3e-dc14813d9bf1","resolution":{"observed_at":"2026-08-06T21:32:57.124397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19666","last_updated":"2024-11-29T12:39:57Z","snapshot_observed_at":"2026-08-02T16:14:35.442247Z","submitted_at":"2024-11-29T12:39:57Z","title":"Multimodal Whole Slide Foundation Model for Pathology","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.19666","snapshot_observed_at":"2026-08-06T21:32:57.192931Z","title":"Multimodal Whole Slide Foundation Model for Pathology,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.192931Z"},"links":{"cited_paper":"/paper/2411.19666","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:7461627a33f2daeb379ac4740905cfd8e5e2e60f130e77917d55ae06edd1c170","observation_id":"78fd05f6-7aa3-4011-8cd3-658e190a0363","resolution":{"observed_at":"2026-08-06T21:32:57.192931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/bib/bbae082","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:31.451892Z","title":"Deep learning in spatially resolved transcriptomics: a comprehensive technical view,","venue":null,"work_id":"1a3a4049-9185-4196-a68a-e1ccccef3a32","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.243210Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:afde9784005cbbb19110eefa03d0fbb21e8c73d08432b999aeade27d1d1cd572","observation_id":"6b3bf9cc-e794-43bc-b576-d30c790cd6bd","resolution":{"observed_at":"2026-08-06T21:33:31.602859Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:57.278702Z","title":"Transformers in single-cell omics: a review and new perspectives,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.278702Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:37fba37bfb7d3b67953cb354ffffbdceea678be44ec2f89bb525cdcb7ba4edde","observation_id":"be1e22fc-21b0-4dad-a9ac-6c999375f74c","resolution":{"observed_at":"2026-08-06T21:32:57.278702Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2025.03.04.641512v1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:31.330830Z","title":"Language of Stains: Tokenization Enhances Multiplex Immunofluorescence and Histology Image Synthesis | bioRxiv","venue":null,"work_id":"e1e474ae-5519-46bd-973d-c345d6e03342","year":2025},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.341945Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:e845c22d89d70fb9557617b4492a93b815a20208a1a09a298f2d9232e67ef704","observation_id":"22aefa0b-c233-4bb2-923b-964dbfc1d103","resolution":{"observed_at":"2026-08-06T21:33:31.374415Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:57.391226Z","title":"scGPT: toward building a foundation model for single-cell multi-omics using generative AI,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.391226Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:5cd8fce3c1dc8242d93f7d7ec3b9ae1ce74f1ead8fddd7f62a99651d556efa0f","observation_id":"83772758-3779-4a76-bfeb-fa36d194b838","resolution":{"observed_at":"2026-08-06T21:32:57.391226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:57.429100Z","title":"scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.429100Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:0e7d4b430aba3192b94919aedd2987e90b4a96642fd93b703242177d96a43bbd","observation_id":"90ffe380-a082-4220-b169-0b725f79521a","resolution":{"observed_at":"2026-08-06T21:32:57.429100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s12864-018-5370-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:31.160702Z","title":"Gene2vec: distributed representation of genes based on co-expression,","venue":null,"work_id":"26549155-f4a0-4240-857d-37c2ca0f06da","year":2019},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.494772Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:bab395ce2df8c3f0fdb67b23ae7ac771b2eba291bf85436854db61cf19b0c833","observation_id":"6a79b9cf-f15f-4aca-9f7f-c69713447a37","resolution":{"observed_at":"2026-08-06T21:33:31.235034Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41592-024-02305-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:31.034793Z","title":"Large-scale foundation model on single-cell transcriptomics,","venue":null,"work_id":"1d3977df-5f2d-4fda-b33d-66a10dcb1158","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.562190Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:85f90f3eaf6dafac6aafbf4395857f032a92b19b1df188d6a443f82ec89f83c7","observation_id":"d45f2b45-4e31-47d3-8e12-167d67cdd2e9","resolution":{"observed_at":"2026-08-06T21:33:31.087181Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:57.710748Z","title":"CellPLM: Pre-training of Cell Language Model Beyond Single Cells,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.710748Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:81521062829f11e043949ee1f9aaf1fa79c4a10eadb7710429cb7aa3ecfcd5fe","observation_id":"3b0f3334-7846-4bc1-b91d-ddd71e6b7979","resolution":{"observed_at":"2026-08-06T21:32:57.710748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:57.769383Z","title":"Generative pretraining from large-scale transcriptomes for single-cell deciphering,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.769383Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:28a3cfd73346c86297d817e94f59aa6899565ea37da6b9a10db5c5805d224722","observation_id":"308d4c4e-8b98-45b9-aff3-b3b45c94ed11","resolution":{"observed_at":"2026-08-06T21:32:57.769383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.15156","last_updated":"2024-02-24T13:03:49Z","snapshot_observed_at":"2026-07-31T21:31:30.712328Z","submitted_at":"2023-11-26T01:23:01Z","title":"xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.15156","snapshot_observed_at":"2026-08-06T21:32:57.810757Z","title":"xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.810757Z"},"links":{"cited_paper":"/paper/2311.15156","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:e3ce763554e4ec90d8c690cfadd9ec680aba0a964efbce2090a1b8cd855ad3c3","observation_id":"4ec75036-2c2e-4274-af93-1acea6ec3af1","resolution":{"observed_at":"2026-08-06T21:32:57.810757Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:57.875814Z","title":"Transformer for one stop interpretable cell type annotation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.875814Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:34143b4da50677fda3b41b270b21bdcdcd289f13d09c960856853e3c6d73d569","observation_id":"6debe3ce-41d2-424b-9d41-256289680f4b","resolution":{"observed_at":"2026-08-06T21:32:57.875814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41586-023-06139-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:30.668547Z","title":"Transfer learning enables predictions in network biology,","venue":null,"work_id":"8d0bba09-7a5f-49f0-9f84-abe6559ff506","year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:57.973676Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:30b56fff9c78f4cedb02945234f7c057cf82dae4f83c46daf7b993a46d7158ad","observation_id":"33cb2b13-74f9-4379-b28c-7e5b0a400e7a","resolution":{"observed_at":"2026-08-06T21:33:30.852994Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.074574Z","title":"Nicheformer: a foundation model for single-cell and spatial omics,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.074574Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:e9675961dc74bec30d6d769621e1cf39d4f30b3ad27328fbff539703dd24407b","observation_id":"ce89a339-c3c3-486e-aa90-eb46d7a74be4","resolution":{"observed_at":"2026-08-06T21:32:58.074574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.03038","last_updated":"2024-02-16T17:42:38Z","snapshot_observed_at":"2026-07-06T14:48:56.286312Z","submitted_at":"2023-02-06T01:44:13Z","title":"Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.03038","snapshot_observed_at":"2026-08-06T21:32:58.151958Z","title":"Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.151958Z"},"links":{"cited_paper":"/paper/2302.03038","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:1533a2df3c7542fdb3b6595cd4ee2b525b2071e3a26f55f06bc843f3f56c9a1d","observation_id":"8f90f112-d26d-4f53-b314-b7a1945ad177","resolution":{"observed_at":"2026-08-06T21:32:58.151958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08224","last_updated":"2024-07-11T06:50:34Z","snapshot_observed_at":"2026-07-06T18:44:37.726622Z","submitted_at":"2024-07-11T06:50:34Z","title":"stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement","version":1},"cited_work":{"arxiv_id":"2407.08224","doi":"10.48550/arxiv.2407.08224","metadata_source":"pith","pith_arxiv_id":"2407.08224","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement","venue":"q-bio.QM","work_id":"45fe466e-7056-4f86-8edf-2f8ddf6b3db1","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.301644Z"},"links":{"cited_paper":"/paper/2407.08224","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:fd6fc37885b6fa43691353c73d095e16292e7f81c93459e396d3ae1a65aa947f","observation_id":"04033ea2-2a5a-4d7d-bf9b-6bd05011e711","resolution":{"observed_at":"2026-08-06T21:33:30.490550Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41467-023-37806-0","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:29.985669Z","title":"Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer,","venue":null,"work_id":"c4dab294-9221-464a-9a3c-888e3bfdb33b","year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.383884Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:a00c77c0122585299f53b567163d6666bb885b3a190db4be439496aed71b40c0","observation_id":"3a9166da-bc7b-4f3b-a6d4-20ef95dcf6d4","resolution":{"observed_at":"2026-08-06T21:33:30.177823Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.ccell.2023.12.012","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:29.729466Z","title":"Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer,","venue":null,"work_id":"4ebcaec1-a54b-4a52-bec8-613a1e253fb7","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.494205Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:a86948cd7af2d34b32895bfc7d83798ab63c9d9fc5d6bbc8d3c880beeaf67b8d","observation_id":"372c64e2-b665-4a4b-8264-f6838f05f6ee","resolution":{"observed_at":"2026-08-06T21:33:29.863551Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.cell.2018.07.010","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:29.399480Z","title":"Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging,","venue":null,"work_id":"18af7285-ad0a-4abe-88bf-1e2dffe92557","year":2018},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.531328Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:606decbffb6871c93660722f73a4dfa5e6c7a71c0905225acbcc8d981bd056fb","observation_id":"b2de96ce-3aef-4a2d-9bcd-9f33ccecd6a8","resolution":{"observed_at":"2026-08-06T21:33:29.543687Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/eji.202048891","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:29.108457Z","title":"Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction,","venue":null,"work_id":"5505abb5-2c4e-4f2f-be2d-1d5c86ca4862","year":2021},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.534147Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:e78e770afc456fa9c36314df8dbf9cec50514c324afdb885180e528efe8e2fea","observation_id":"33b75b7d-16b1-4978-a8d6-297f3129f559","resolution":{"observed_at":"2026-08-06T21:33:29.256294Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/nmeth.2869","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:33:28.939980Z","title":"Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry,","venue":null,"work_id":"74203aa7-7bc6-4ce0-b8b7-a7dc34fa99dd","year":2014},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.536824Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:bfcc1ff5f23e0e2ff459b045ee9feffd4d7e7a1b08e1911ddd399b10144f228b","observation_id":"b0bb28e3-e6fa-464b-a2e0-c5c57492be02","resolution":{"observed_at":"2026-08-06T21:33:29.025864Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41596-021-00644-9","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.500893Z","title":"IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues,","venue":null,"work_id":"57b8324b-4030-4f93-9d5f-75bbec748979","year":2022},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.539288Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:0e11465ae83f51a49bcc7701eac87c5c6410830b165d0531554db03b598c6182","observation_id":"a9fea2e3-9602-47d1-9b82-651ef307c81f","resolution":{"observed_at":"2026-08-06T21:32:59.503838Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s42003-024-06902-2","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.491813Z","title":"Computational immune synapse analysis reveals T-cell interactions in distinct tumor microenvironments,","venue":null,"work_id":"6a86a458-3994-4266-b0dd-f16c82660189","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.542323Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:f570ad53ae75798db52f3446ba3802eb6b63f58e93d6ac3cb5b080ca3db0ac99","observation_id":"983264e1-4f41-4e76-9080-0de9bccf3731","resolution":{"observed_at":"2026-08-06T21:32:59.495062Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2025.04.18.649541","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.483834Z","title":"Prediction of Outcome from Spatial Protein Profiling of Triple- Negative Breast Cancers,","venue":null,"work_id":"b1def181-0d70-4b73-abed-2524667e6e6d","year":2025},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.544801Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:7eecd474f0c81bdc051939e525f5108a94ba1cece6c136c2145aa258e5c0e0a7","observation_id":"0687cfff-bb16-44ca-8347-af9a2afbe6da","resolution":{"observed_at":"2026-08-06T21:32:59.486571Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41587-025-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.475754Z","title":"High-dimensional imaging using combinatorial channel multiplexing and deep learning,","venue":null,"work_id":"743406d5-94d7-44bf-96ed-a3703ff0174d","year":2025},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.547538Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:bf8a36193e26d5c2fa6872b21bbf93584f8558426d9e21555a32da512f2c9ff7","observation_id":"704c8093-3c40-4b8f-88b6-387e0ee2f5f7","resolution":{"observed_at":"2026-08-06T21:32:59.478373Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2025.03.14.643377","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.467448Z","title":"Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes,","venue":null,"work_id":"12f52479-89de-4afd-aac6-67261bea750d","year":2025},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.550057Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:9716495e8ac9f970b849b99c0222b053e9a563fd6fc2696c1297e5cdb7ee8110","observation_id":"c9c84c0f-eb45-4689-9771-18e6530db32f","resolution":{"observed_at":"2026-08-06T21:32:59.470446Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.03373","last_updated":"2025-06-03T20:30:25Z","snapshot_observed_at":"2026-08-07T11:02:22.273838Z","submitted_at":"2025-06-03T20:30:25Z","title":"A Foundation Model for Spatial Proteomics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.03373","snapshot_observed_at":"2026-08-06T21:32:58.552398Z","title":"A Foundation Model for Spatial Proteomics,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.552398Z"},"links":{"cited_paper":"/paper/2506.03373","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:222c47da07bb7505b4c151a55bbf10d56302e4ef5fe8cb99419985a72bb6a61f","observation_id":"5db27af6-4dd1-41b6-a447-78283f012dcf","resolution":{"observed_at":"2026-08-06T21:32:58.552398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.555186Z","title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.555186Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:110eedb459c5a22121c0aedaf57821ab653b460601e42c3a2ea0e941f2a3b7a3","observation_id":"fb6f968c-ae0a-4789-a066-6a7b3ec8709d","resolution":{"observed_at":"2026-08-06T21:32:58.555186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07538","last_updated":"2018-12-03T22:15:26Z","snapshot_observed_at":"2026-08-06T12:18:11.194277Z","submitted_at":"2018-06-20T03:47:03Z","title":"Towards Robust Interpretability with Self-Explaining Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07538","snapshot_observed_at":"2026-08-06T21:32:58.557764Z","title":"Towards Robust Interpretability with Self- Explaining Neural Networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.557764Z"},"links":{"cited_paper":"/paper/1806.07538","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:a897a3d851528f7cd7bdbd6320bec054f9e7baa0f48ecc9fa8b1962d2b91164c","observation_id":"2acaebf9-fd39-4763-b059-0d7b414a05fe","resolution":{"observed_at":"2026-08-06T21:32:58.557764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.560432Z","title":"Attention is not Explanation,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.560432Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:bd617c4d004169fba5f48d80f95a951a81a0e919cff02e72ce37a4dc4b15341b","observation_id":"65a5a667-c197-4e16-bca0-103ced10a799","resolution":{"observed_at":"2026-08-06T21:32:58.560432Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.562972Z","title":"Is Attention Interpretable?,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.562972Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:4ad56ff1afcc61e828a5a91db199ba1162d3e387610a30e8728b23b9613a2109","observation_id":"12c90ef2-e6da-4365-b84e-222aaf509173","resolution":{"observed_at":"2026-08-06T21:32:58.562972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05656","last_updated":"2021-06-03T06:51:16Z","snapshot_observed_at":"2026-08-01T08:43:04.363891Z","submitted_at":"2020-06-10T05:08:30Z","title":"Why Attentions May Not Be Interpretable?","version":4},"cited_work":{"arxiv_id":"2006.05656","doi":"10.48550/arxiv.2006.05656","metadata_source":"pith","pith_arxiv_id":"2006.05656","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"Why Attentions May Not Be Interpretable?","venue":"stat.ML","work_id":"a5e0114a-298a-4ee7-8b48-044d55affaee","year":2020},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.565513Z"},"links":{"cited_paper":"/paper/2006.05656","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:cd462a6e207257ed3db1b78ca42d059e779885858402588f31f441fd09455d1b","observation_id":"68aad919-85d9-4742-be75-908c6d00a56b","resolution":{"observed_at":"2026-08-06T21:32:59.430333Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07874","last_updated":"2017-11-25T03:53:32Z","snapshot_observed_at":"2026-07-06T05:43:42.722222Z","submitted_at":"2017-05-22T17:38:10Z","title":"A Unified Approach to Interpreting Model Predictions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07874","snapshot_observed_at":"2026-08-06T21:32:58.568249Z","title":"A Unified Approach to Interpreting Model Predictions,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.568249Z"},"links":{"cited_paper":"/paper/1705.07874","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:670f3992e15925731ecc86e5e8e3d604eadd120491e0753ce9c40d2ca68d5576","observation_id":"bd31b22d-4960-45ef-8251-27eb6f1d03eb","resolution":{"observed_at":"2026-08-06T21:32:58.568249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1602.04938","last_updated":"2016-08-09T17:54:52Z","snapshot_observed_at":"2026-07-06T04:46:17.931363Z","submitted_at":"2016-02-16T08:20:14Z","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.04938","snapshot_observed_at":"2026-08-06T21:32:58.570809Z","title":"‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.570809Z"},"links":{"cited_paper":"/paper/1602.04938","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:ad3ffbbef1077bc6b56a8e5579ffd0f4166c6f26bbbf35ea029e6ac9d96a760c","observation_id":"f1c75a1f-84a1-4b49-97b1-bb69ee6111f8","resolution":{"observed_at":"2026-08-06T21:32:58.570809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.10687","last_updated":"2025-03-12T00:12:27Z","snapshot_observed_at":"2026-08-07T17:11:29.995783Z","submitted_at":"2025-03-12T00:12:27Z","title":"Context-guided Responsible Data Augmentation with Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.10687","snapshot_observed_at":"2026-08-06T21:32:58.573632Z","title":"Context-guided Responsible Data Augmentation with Diffusion Models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.573632Z"},"links":{"cited_paper":"/paper/2503.10687","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:262ee94f37a4e9976abeb1c031e3f4b651341d242f40b1c5afd7256d77fd89e8","observation_id":"ba6c0557-107c-4bb1-86a8-030dcad6e729","resolution":{"observed_at":"2026-08-06T21:32:58.573632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.576472Z","title":"Thermodynamics-inspired explanations of artificial intelligence,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.576472Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:3ec00469daf2f2bca0630554709bd6beb906d59872d92c3319763a48b1b8f079","observation_id":"9963d856-f6ec-41d2-a5d0-5a7cb1ffcead","resolution":{"observed_at":"2026-08-06T21:32:58.576472Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s42003-022-03644-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.386078Z","title":"Tissue registration and exploration user interfaces in support of a human reference atlas,","venue":null,"work_id":"6aed2109-ae6f-4e86-8411-8415a0d4eb27","year":2022},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.579127Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:37c427e604620d9e846d3b434d7eeed95c01758e78bc7c0fadf24cc848f43c99","observation_id":"6f090a87-5eb8-4ef0-812a-61d39494fe0d","resolution":{"observed_at":"2026-08-06T21:32:59.389226Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41556-023-01194-w","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.376597Z","title":"Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP),","venue":null,"work_id":"df6c0ab0-2c8b-4052-b986-cfba5711f77e","year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.581743Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:75b434e830d2b229c9ae7dd552c88c1d22b9b9006dd158e50e36d1ace00eaeb6","observation_id":"96bb0fd0-179c-4471-855b-78b7fc720af6","resolution":{"observed_at":"2026-08-06T21:32:59.379971Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[{"edge_observation":{"observed_at":"2026-08-07T06:19:14.190366+00:00","source":"paper_reference_links","state":"open"},"event_date":"2024-03-01","event_type":"correction","notice_doi":"10.1038/s41556-024-01384-0","provenance":{"observed_at":"2026-07-11T03:01:20.407983+00:00","source":"crossref","source_record_id":"10.1038/s41556-024-01384-0->10.1038/s41556-023-01194-w:correction"}}],"reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.21203/rs.3.rs-3952048/v1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.367594Z","title":"Graph Fourier transform for spatial omics representation and analyses of complex organs,","venue":null,"work_id":"901719b9-a9a0-4e9d-9df2-8d8900d9978b","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.584839Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:a29b038d1132f94eda2a5b9e4df3f69eb3691befcab226722ce9108572ee34f3","observation_id":"8cb7c7f2-363f-47b2-8e15-ecce6df3589d","resolution":{"observed_at":"2026-08-06T21:32:59.370855Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s43018-020-0026-6","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.359014Z","title":"Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer,","venue":null,"work_id":"ae236fa4-4fea-4758-af07-14ffe7cb468a","year":2020},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.587632Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:f9f2bbf209a4dd5c1293a797db09b6245fcbf9fc97e80a33e501d97c2deb8341","observation_id":"435a5ba1-0089-47b1-aee7-f73d5807cb4e","resolution":{"observed_at":"2026-08-06T21:32:59.361737Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41586-019-1876-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.350416Z","title":"The single-cell pathology landscape of breast cancer,","venue":null,"work_id":"8997ee4a-4f9e-48a4-bd78-bccff4897c8e","year":2020},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.590579Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:5923f8087b3e630b1b87a59751f43f3c0b9bf34b167ea3dab2852ae2a8c31297","observation_id":"32963a9d-a39b-4fd2-97d5-0fa878641686","resolution":{"observed_at":"2026-08-06T21:32:59.353166Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.cell.2007.05.025","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.341967Z","title":"Probing the Limits to Positional Information,","venue":null,"work_id":"6838ebbc-1eb9-4d83-b72f-4ef57b03441d","year":2007},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.593039Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:d1b086a1f9ce10761c75eef8b3df6cf40e564db938495a944d5f0f8f24925788","observation_id":"0b316e6d-8482-4bb3-b5b6-e8958f6f6d93","resolution":{"observed_at":"2026-08-06T21:32:59.344738Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.595490Z","title":"Decoding cellular communication: An information theoretic perspective on cytokine and endocrine signaling,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.595490Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:658503ad40f664119189c33bca990fcb6579d5efb411b5408b857beb09ee086c","observation_id":"5b4ab6cf-b009-4efc-83d9-43178246cfba","resolution":{"observed_at":"2026-08-06T21:32:58.595490Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.trecan.2020.12.013","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.333426Z","title":"Concepts and Applications of Information Theory to Immuno-Oncology,","venue":null,"work_id":"efc5755c-24b1-4fda-ad09-2ec3ec518afc","year":2021},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.597987Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:25225f0d5c4ef235213a4995a80bc19c656775cd28404e742b8b1cfa38c75ecb","observation_id":"229a945d-572a-41b9-a795-415e180b0a61","resolution":{"observed_at":"2026-08-06T21:32:59.336563Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.cell.2024.03.029","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.325722Z","title":"Integrative spatial analysis reveals a multi-layered organization of glioblastoma,","venue":null,"work_id":"05cabfab-acaa-4dfe-bdd2-3cfa46252021","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.600612Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:97cb95176f8c14205fe1bd67dc61ccdeb2c140ec10c6ef299e9b53c64a00afde","observation_id":"875ed1e4-a418-48ff-9d33-6458cfe55f43","resolution":{"observed_at":"2026-08-06T21:32:59.328342Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"physics/0004057","last_updated":"2000-04-24T15:22:30Z","snapshot_observed_at":"2026-08-07T10:46:08.274157Z","submitted_at":"2000-04-24T15:22:30Z","title":"The information bottleneck method","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"physics/0004057","snapshot_observed_at":"2026-08-06T21:32:58.603090Z","title":"The information bottleneck method,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.603090Z"},"links":{"cited_paper":"/paper/physics/0004057","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:fa8bfb63f159f9aa13232f8601be6bb3e752e025388681df63179594d507fdc1","observation_id":"9bbcfd7a-2ceb-4426-90df-79a22ca0969b","resolution":{"observed_at":"2026-08-06T21:32:58.603090Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2024.05.22.595292","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.309106Z","title":"Identifying maximally informative signal- aware representations of single-cell data using the Information Bottleneck,","venue":null,"work_id":"5cbe1a66-e499-4d5f-9011-a69626ed7e6b","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.606015Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:537a6fe5ee4bdd00cc2eb4b7ab8f35886a2ee9dfddff524221f5e09d8dacd4de","observation_id":"f6093e94-454f-48aa-a459-57e9b8568fce","resolution":{"observed_at":"2026-08-06T21:32:59.311971Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02406","last_updated":"2015-03-09T09:39:41Z","snapshot_observed_at":"2026-07-06T04:11:22.270946Z","submitted_at":"2015-03-09T09:39:41Z","title":"Deep Learning and the Information Bottleneck Principle","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02406","snapshot_observed_at":"2026-08-06T21:32:58.608599Z","title":"Deep Learning and the Information Bottleneck Principle,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.608599Z"},"links":{"cited_paper":"/paper/1503.02406","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:312106381f765798141df1018bdcf3c49c6545773514d80f3740087acb293dba","observation_id":"07bcbd86-fb96-4d9a-886b-49e35fb51711","resolution":{"observed_at":"2026-08-06T21:32:58.608599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/e24010135","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.293267Z","title":"An Information Theoretic Interpretation to Deep Neural Networks,","venue":null,"work_id":"a4e93ca1-8c81-4215-a921-b4f4ad8e688b","year":2022},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.611582Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:9dcb7802ad0e946454b93a269cd3559a8e1bd279a70c9ce895c4454abd5284d3","observation_id":"05179b90-e6dc-4252-a94c-92371c30f31a","resolution":{"observed_at":"2026-08-06T21:32:59.296153Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.614085Z","title":"DALL·E 3","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.614085Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:c1664e064205ae2c3e0fa375174200c76353968c13d3974cbbc3e0da16959934","observation_id":"0809d4c4-4bdd-4ef9-bacf-53757a196ea0","resolution":{"observed_at":"2026-08-06T21:32:58.614085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.616518Z","title":"Stability AI Image Models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.616518Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:82352e07b96fb418fd472fbab4e101d11eb0342d7aa76c1f8315a2691c2ea1d7","observation_id":"13a05c65-933f-4bbd-bdf7-ee31d3f94374","resolution":{"observed_at":"2026-08-06T21:32:58.616518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-06T21:32:58.618898Z","title":"Score- Based Generative Modeling through Stochastic Differential Equations,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.618898Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:c819becd14f01b9d45146522990b3d27fddb09bd245c684c206f1db5b663fde9","observation_id":"1195b7ae-1ceb-497d-b12d-905d52f57b27","resolution":{"observed_at":"2026-08-06T21:32:58.618898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10752","last_updated":"2022-04-13T11:38:44Z","snapshot_observed_at":"2026-07-06T12:20:47.369918Z","submitted_at":"2021-12-20T18:55:25Z","title":"High-Resolution Image Synthesis with Latent Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10752","snapshot_observed_at":"2026-08-06T21:32:58.621709Z","title":"High-Resolution Image Synthesis with Latent Diffusion Models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.621709Z"},"links":{"cited_paper":"/paper/2112.10752","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:9458929c2b6bf710af0e13ab24f368a72311b9034ba937d5bfe2ae56e4bb6dd8","observation_id":"9bb60674-b408-4c7d-8cd1-a5f50dd64b02","resolution":{"observed_at":"2026-08-06T21:32:58.621709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.09748","last_updated":"2023-03-02T09:06:55Z","snapshot_observed_at":"2026-07-06T14:32:37.317828Z","submitted_at":"2022-12-19T18:59:58Z","title":"Scalable Diffusion Models with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.09748","snapshot_observed_at":"2026-08-06T21:32:58.624510Z","title":"Scalable Diffusion Models with Transformers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.624510Z"},"links":{"cited_paper":"/paper/2212.09748","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:de2aa11fe77d7ab3fa1ad6f518572e38d69df0be552f5ae6776a3459fd96c1ce","observation_id":"4b5fe832-bdbf-40db-a3b4-474340fe05d8","resolution":{"observed_at":"2026-08-06T21:32:58.624510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.627231Z","title":"stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics | Briefings in Bioinformatics | Oxford Academic","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.627231Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:c3f417bc9015a02b4081c317fed1c589a7225948f57de18aa41bf17b6d1b4be3","observation_id":"0ab5bd42-ee66-4d08-a0ef-a04b1fbf7bb1","resolution":{"observed_at":"2026-08-06T21:32:58.627231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13182","last_updated":"2024-07-18T05:40:50Z","snapshot_observed_at":"2026-07-06T18:48:16.629078Z","submitted_at":"2024-07-18T05:40:50Z","title":"SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq","version":1},"cited_work":{"arxiv_id":"2407.13182","doi":"10.48550/arxiv.2407.13182","metadata_source":"pith","pith_arxiv_id":"2407.13182","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq","venue":"cs.LG","work_id":"7cfcc41f-64d1-4623-855a-0ff006658cf1","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.632645Z"},"links":{"cited_paper":"/paper/2407.13182","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:369d6a7d1ed4c36a2d31fdf613988df6be558b3c7369f1cdd564e63724a885e5","observation_id":"ff849d57-c4a6-4489-97d5-21b10ced37f2","resolution":{"observed_at":"2026-08-06T21:32:59.269402Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2024.06.19.599672","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.254573Z","title":"DiffuST: a latent diffusion model for spatial transcriptomics denoising,","venue":null,"work_id":"0fc356c7-68f7-4bca-bb26-5fb982113930","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.635736Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:fdea84df470e9c8942b8e89cadc02afc540bde3c31987de388d1c7c90992c5a7","observation_id":"85073df1-06d4-4aa8-b175-d11b29ee291f","resolution":{"observed_at":"2026-08-06T21:32:59.257360Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2024.05.21.595094","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.246161Z","title":"SpatialDiffusion: Predicting Spatial Transcriptomics with Denoising Diffusion Probabilistic Models,","venue":null,"work_id":"8fa7b2dc-65fd-4ef9-b1cc-6a4a14831028","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.638201Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:b72430fddcda6b75b87f92d553a5d5739ba29fa571d3382fd721901bd8881810","observation_id":"e51f5b4e-beda-40f5-abb8-f2017abc9a51","resolution":{"observed_at":"2026-08-06T21:32:59.249166Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.10863","last_updated":"2024-03-16T09:06:38Z","snapshot_observed_at":"2026-07-06T17:45:39.785241Z","submitted_at":"2024-03-16T09:06:38Z","title":"stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation","version":1},"cited_work":{"arxiv_id":"2403.10863","doi":"10.48550/arxiv.2403.10863","metadata_source":"pith","pith_arxiv_id":"2403.10863","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation","venue":"q-bio.GN","work_id":"3f5a75a0-a286-437d-81e7-cc05f6fcc506","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.640728Z"},"links":{"cited_paper":"/paper/2403.10863","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:d112c68860b2cbd75e477edd83afe3ce23357df659a71e1b59b41c4580b29f8e","observation_id":"8a2405d6-bdce-4db6-ba2e-b16cb2464f80","resolution":{"observed_at":"2026-08-06T21:32:59.239888Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.643457Z","title":"The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.643457Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:5ce45cb0ee72d81a75319be6b12913a75eef8e9416eb2f5a2252bf570ee0d7a7","observation_id":"c4ef83c4-ad4c-4aea-afed-a502c1a62351","resolution":{"observed_at":"2026-08-06T21:32:58.643457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.648780Z","title":"Generative diffusion in very large dimensions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.648780Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:3868d4720696bf06c46dbdd2603d93e20a27b99b74c875cd180a99f401b15be5","observation_id":"83889c91-9a14-414b-ac80-29fadff6270d","resolution":{"observed_at":"2026-08-06T21:32:58.648780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17467","last_updated":"2024-06-20T08:39:11Z","snapshot_observed_at":"2026-07-06T16:39:00.553251Z","submitted_at":"2023-10-26T15:15:01Z","title":"The statistical thermodynamics of generative diffusion models: Phase transitions, symmetry breaking and critical instability","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.17467","snapshot_observed_at":"2026-08-06T21:32:58.646046Z","title":"Available: https://arxiv.org/abs/2310.17467v4","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.646046Z"},"links":{"cited_paper":"/paper/2310.17467","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:c14f62a69d72c00d0ad0dad4890687946f77b1fb6116c917d7ad46904e292174","observation_id":"286afc8d-2e49-4e66-a5d3-4afb309c3974","resolution":{"observed_at":"2026-08-06T21:32:58.646046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2023.10.10.561757","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.208240Z","title":"Mapping the topography of spatial gene expression with interpretable deep learning,","venue":null,"work_id":"9189ac19-dec5-44ce-a300-657ddcf3c3bf","year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.653863Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:6af311499e8d91820b300967b321a27f6cf3fbe613196079180a70045cc5d704","observation_id":"fc9efb8a-e89b-4a4a-9a08-eaf263cb0c3d","resolution":{"observed_at":"2026-08-06T21:32:59.216752Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.651282Z","title":"A phase transition in diffusion models reveals the hierarchical nature of data,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.651282Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:4eb07d758fb90a1b988b1ceecfb56dce7532bbc68ef27feecf1a44b70832163e","observation_id":"c7e605dc-2474-48e3-9d3d-e2d2c30ba88c","resolution":{"observed_at":"2026-08-06T21:32:58.651282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.658709Z","title":"A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.658709Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:7746f2ce5aa092786209aa32d6e6ed22cb078d34b8836977badb5b10f370e3ca","observation_id":"51d40fa8-2a0f-4531-8689-6f6360d9c4c2","resolution":{"observed_at":"2026-08-06T21:32:58.658709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.656383Z","title":"A Reaction-Diffusion Model of Cancer Invasion,","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.656383Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:709304ad583241b33b65a93d2f6144bc15f66d86af815cf86915806a4e474664","observation_id":"4db2c751-f98f-4546-95e8-ee26230e11f0","resolution":{"observed_at":"2026-08-06T21:32:58.656383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.663479Z","title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.663479Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:c377037dc357de0eccc158f3990bfbfc30757d1de76f7297b280069cc3922da4","observation_id":"4f4f34ac-d7c5-49f5-a7c1-9a63ed77a94a","resolution":{"observed_at":"2026-08-06T21:32:58.663479Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.661012Z","title":"A mechanically coupled reaction–diffusion model that incorporates intra-tumoural heterogeneity to predict in vivo glioma growth,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.661012Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:a458214bc1e3fa35357899e997adc98f9513b4df4a844f9d72191cb39419f716","observation_id":"129c5a2c-8edf-49ab-bcef-a0086b67fd36","resolution":{"observed_at":"2026-08-06T21:32:58.661012Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2023.11.17.567500","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.186903Z","title":"NeuroVelo: interpretable learning of temporal cellular dynamics from single-cell data,","venue":null,"work_id":"8213d529-5c59-42a9-bdb1-f5ef5e66fe05","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.669434Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:cee8c674c3c613be400de58ac70ee8a1b3f69acc1307341d95c5b0f515a49740","observation_id":"9f792490-2ded-4ca2-9b0b-1551d501af30","resolution":{"observed_at":"2026-08-06T21:32:59.189711Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.666627Z","title":"A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.666627Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:0092aa72d065d25ed04dfc796d5e2595555e2e58c57443a1668062db67475bcf","observation_id":"c44f5b67-686f-408e-84c5-3b9ede7b8f41","resolution":{"observed_at":"2026-08-06T21:32:58.666627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.674661Z","title":"SpaCCC: Large language model-based cell-cell communication inference for spatially resolved transcriptomic data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.674661Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:0b88cf2f24a3d1071830e0a022cbce204e6a9fbbb229b3bd9bdaefe63cd9798e","observation_id":"8ba13b83-80ba-4a41-9ebb-23fa1b8670ac","resolution":{"observed_at":"2026-08-06T21:32:58.674661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/bib/bbad359","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.177045Z","title":"Decoding functional cell–cell communication events by multi-view graph learning on spatial transcriptomics,","venue":null,"work_id":"7c5d8078-d388-403b-a816-e060fe7e0eef","year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.671859Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:0bcc341f7dd60dd9eb32ab8740c2d175b8f1a36446416f7e64891e662cfd39b7","observation_id":"df6b0cca-727d-4c45-a65b-e25f647f3da9","resolution":{"observed_at":"2026-08-06T21:32:59.180035Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.15719","last_updated":"2024-06-22T03:31:02Z","snapshot_observed_at":"2026-08-06T04:49:33.584006Z","submitted_at":"2024-06-22T03:31:02Z","title":"How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification","version":1},"cited_work":{"arxiv_id":"2406.15719","doi":"10.48550/arxiv.2406.15719","metadata_source":"pith","pith_arxiv_id":"2406.15719","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification","venue":"cs.CV","work_id":"8850248f-419d-4035-af5b-3880092af3c3","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.679778Z"},"links":{"cited_paper":"/paper/2406.15719","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:a3dce5f5ee94368a93f0430451a4bb94acc26453ca9ed6445219d78f80920c80","observation_id":"cbf9609a-629d-4fdf-8d13-df6325c8cfc3","resolution":{"observed_at":"2026-08-06T21:32:59.164910Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.19756","last_updated":"2025-02-09T21:09:09Z","snapshot_observed_at":"2026-07-06T18:07:47.744531Z","submitted_at":"2024-04-30T17:58:29Z","title":"KAN: Kolmogorov-Arnold Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19756","snapshot_observed_at":"2026-08-06T21:32:58.677177Z","title":"KAN: Kolmogorov–Arnold Networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.677177Z"},"links":{"cited_paper":"/paper/2404.19756","citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:462b827338520dd9f82cc8df6ecae417887db1be57301dcafc9814e700a91687","observation_id":"52f53d04-8d5d-425b-ae56-a23e4f828b9a","resolution":{"observed_at":"2026-08-06T21:32:58.677177Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s42254-024-00707-2","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.141742Z","title":"Mechanical properties of human tumour tissues and their implications for cancer development,","venue":null,"work_id":"23712800-4f76-49a6-99f8-bcf16e733f47","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.685275Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:48a5830497a6c0a051631539b818287158d58fd6ca3b8162d7a6d4ecdeeacd42","observation_id":"c9c9baa1-50eb-48dd-980e-cac875639c28","resolution":{"observed_at":"2026-08-06T21:32:59.144566Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/bioinformatics/btab164","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.150234Z","title":"sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling,","venue":null,"work_id":"602ec5d9-b94f-4a18-baee-87eb3a711cea","year":2021},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.682670Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:15d96a50cb5547d5f521b2f4cbdff9584739a2b33d495a12563518536c932e8b","observation_id":"a40d1574-fdf5-429b-8ea6-53c7237f125b","resolution":{"observed_at":"2026-08-06T21:32:59.153012Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1074/jbc.rev119.007759","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.123082Z","title":"Cell adhesion in cancer: Beyond the migration of single cells,","venue":null,"work_id":"a254134f-a0a5-4f61-a9ab-24821549aee1","year":2020},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.690284Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:c74ab15edc8b4bd08da5941f492377d042edf200804d53cafaef4222765a5db7","observation_id":"4fd95719-588c-46fe-993b-19e85d960291","resolution":{"observed_at":"2026-08-06T21:32:59.126208Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41419-024-06697-4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.132697Z","title":"Modulating extracellular matrix stiffness: a strategic approach to boost cancer immunotherapy,","venue":null,"work_id":"390a8ab2-87e5-43e1-86ea-c76166ce585a","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.687805Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:2d22455168b6d03d15d9482bfd95240fad5e4e4bbd6067a97c0ff7fdc7119c1a","observation_id":"74d60c4a-53df-4f98-94c3-cf48c51ec489","resolution":{"observed_at":"2026-08-06T21:32:59.136234Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-319-95294-9_6","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.104458Z","title":"Mechanical Forces in Tumor Angiogenesis,","venue":null,"work_id":"98be3c5c-053f-4723-bc63-7f5136bfd58c","year":2018},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.695621Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:d39467fdb4cd38d5fbba5872142022b57d24a839a0b8b6d828ba57b57a6c6cfe","observation_id":"8a4a5cde-23b2-4b23-a7da-7e78357dc55a","resolution":{"observed_at":"2026-08-06T21:32:59.107447Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/nrc3080","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.113603Z","title":"The physics of cancer: the role of physical interactions and mechanical forces in metastasis,","venue":null,"work_id":"7f5ec94f-b8a4-4838-bde1-a36e23596be4","year":2011},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.692989Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:43236a0ef40c0e5b246efd034b3f864b6e8d1f48b16224aad5251f2be86f32b9","observation_id":"7aecaee0-e50c-4b2d-a0fc-a9ce4f8a8601","resolution":{"observed_at":"2026-08-06T21:32:59.116647Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1063/5.0186042","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.087117Z","title":"Advances in cancer mechanobiology: Metastasis, mechanics, and materials,","venue":null,"work_id":"fe8aec75-ace1-41b5-891e-16d344f3506f","year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.700546Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:5e6ca9a72af4f033e8ffb766e6dd719437043bea781b5621a796c25f48ccac64","observation_id":"9fa86a7d-f39d-4855-82e0-68850d9ce3e3","resolution":{"observed_at":"2026-08-06T21:32:59.090206Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2023.08.03.551894","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:59.095788Z","title":"A computational pipeline for spatial mechano-transcriptomics,","venue":null,"work_id":"27db9c1f-93f5-429d-b153-e638a1078d9c","year":2023},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.698262Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:ac5e8f566a70f8438f4896edab5e235580699c582e4e5ce6ff1891b8804afa8a","observation_id":"0021d107-81fc-4e17-b886-ca8b2e98bc63","resolution":{"observed_at":"2026-08-06T21:32:59.098900Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:32:58.705589Z","title":"Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-06T21:32:58.705589Z"},"links":{"citing_paper":"/paper/2506.23857"},"observation_digest":"sha256:2e8effa09614aa5306008695fd717ac2345ae616ca6ebd4d253b01eabd40cf5a","observation_id":"52f6cd1c-7359-4d95-af96-4e08b827a7b5","resolution":{"observed_at":"2026-08-06T21:32:58.705589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.23857","last_updated":"2025-06-30T13:51:25Z","latest_version":1,"primary_category":"q-bio.QM","snapshot_observed_at":"2026-08-06T21:27:19.777873Z","submitted_at":"2025-06-30T13:51:25Z","title":"Emerging AI Approaches for Cancer Spatial Omics"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":7,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":50,"verified_exact":43,"verified_fuzzy":0},"total_outbound_references":135},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 100 of 135 outbound references and 0 inbound Pith citation observations for arXiv:2506.23857."}