{"as_of":"2026-08-22T17:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bed496ce759f92f73d4ee5cf2c52f62a61430693251d02c7bd0a237d14b59514","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T22:39:04.206386Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2412.03278/citation-record","integrity":"/paper/2412.03278/integrity","json":"/paper/2412.03278/citation-record.json","paper":"/paper/2412.03278"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:07.501210Z","title":"European Journal of Human Genetics, 26 0 (10): 0 1537--1546, 2018","venue":null,"work_id":"48157f19-e7e8-406a-ace8-dc098da7a72f","year":2018},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:02.938941Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:9be2ff26cf54da89eaa441c540c8b37a612a4fc71c34c6d9f215e76f5934a204","observation_id":"67e4dd83-19c5-4220-bc24-3101c6705e1b","resolution":{"observed_at":"2026-08-11T22:39:07.516679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:07.474303Z","title":"Genome-ac-gan: Enhancing synthetic genotype generation through auxiliary classification","venue":null,"work_id":"24c1f824-ade8-4488-92a0-6f5ea415e6d7","year":2024},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:02.985673Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:1020b5d3c3ab4cf502c9bc759a065538ff4b61ca5bfebe5a9d3d64f1f0a87961","observation_id":"36767eaa-1760-44b0-a883-3963f6d69709","resolution":{"observed_at":"2026-08-11T22:39:07.490869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:07.457419Z","title":"Imputation of exome sequence variants into population-based samples and blood-cell-trait-associated loci in african americans: Nhlbi go exome sequencing project","venue":null,"work_id":"f46158f9-b942-4ca4-b03d-86b97cf47cc0","year":2012},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.011944Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:00d155b0c342f5fe1107a5bfb0b726037fef445350eee2a1c8e1d9a40da4592b","observation_id":"b4341582-ad3c-4dd0-9d80-c5bcbabb427b","resolution":{"observed_at":"2026-08-11T22:39:07.461098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:07.440700Z","title":"Dirichlet diffusion score model for biological sequence generation","venue":null,"work_id":"c6a79de5-d4b3-4523-a033-0b1f393ce434","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.034836Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:ea892b634e1c62a402159cb98051d8234b9ea6f309dedcc22e0026abfae2c326","observation_id":"3cbfb7a6-54b8-4224-8901-6c96a2c34bc7","resolution":{"observed_at":"2026-08-11T22:39:07.445753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:07.314634Z","title":null,"venue":null,"work_id":"c79422d0-ab92-4eee-88e2-dc56092eaba4","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.166186Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:421a2a06a60ce4c7c5cb94d31e70361275d605ce7cc228db869651e8028f47e3","observation_id":"f60dd2a9-bae6-4150-b421-6229f7f20be5","resolution":{"observed_at":"2026-08-11T22:39:07.428861Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:07.190834Z","title":"Generating realistic artificial human genomes using adversarial autoencoders","venue":null,"work_id":"2299d49e-50e0-4d1c-b317-9896e1202958","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.254068Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:1d78c9e312c36d09d2d6f749e5f51a682088a3d62995dd43c3f36227a4f7d44b","observation_id":"fa96f723-b6d4-443d-b9a2-3aa94850bfb8","resolution":{"observed_at":"2026-08-11T22:39:07.198108Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:07.129869Z","title":"Accurate proteome-wide missense variant effect prediction with alphamissense","venue":null,"work_id":"8e8bdfa7-0646-4b8d-94f0-c753f6a52fbd","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.327678Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:f31a2f9aadb7a1636696ca76a8827f0f2fbd14a33133de1eed5972937841a1bd","observation_id":"cfc6fa49-33ed-4fd4-850d-e8e2d5411708","resolution":{"observed_at":"2026-08-11T22:39:07.152486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:07.099649Z","title":"A global reference for human genetic variation","venue":null,"work_id":"2dbbb405-20cc-45c1-8aad-41bc0cba1597","year":2015},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.330930Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:52b2d3385678dfe5902b6b6308066414bb6ee873d5da419db0952ca3f022a35e","observation_id":"9592bce3-1cc8-43ef-96a2-45b9a3e7c3d7","resolution":{"observed_at":"2026-08-11T22:39:07.105079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:07.057444Z","title":"Tractable and expressive generative models of genetic variation data","venue":null,"work_id":"6f972991-3cf4-4210-81fe-92008f34717b","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.335603Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:050228397d7718ef14320592fb38b59546628a970fa17d5b2258829e72a6078b","observation_id":"fb19cec3-f1bc-4f80-86b7-2e6d8825e446","resolution":{"observed_at":"2026-08-11T22:39:07.068054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:39:03.341823Z","title":"BERT: pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.341823Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:fe98ca3228861ca2938131a9db5a9de28002108d056d452936da538573558714","observation_id":"9d795ef7-2d3c-47fd-9f6b-7714a174ee6d","resolution":{"observed_at":"2026-08-11T22:39:03.341823Z","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":"2026-08-11T22:39:03.347693Z","title":"Diffusion models beat gans on image synthesis","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.347693Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:de7c9005f76c6c271b5c5ad2cd72cf8ba51f8797e756e7bb2847534cd0bcec28","observation_id":"8ab516a2-825d-4f95-9f9b-35c5a54a4a72","resolution":{"observed_at":"2026-08-11T22:39:03.347693Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:06.825201Z","title":"Detection of long repeat expansions from pcr-free whole-genome sequence data","venue":null,"work_id":"b666c207-3b0f-4bf3-8152-9044e4e5609e","year":1903},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.357534Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:446f95eb70609a8368e365ac023a2f7649d1d05123c63d5c8b503bdf036a3dab","observation_id":"e16c940e-9059-401d-8baf-1c96a35c8dd4","resolution":{"observed_at":"2026-08-11T22:39:06.914758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-16T09:25:53.087782Z","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":"2026-08-11T22:39:03.366484Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.366484Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:df703dc3c2221ae3269d3bc3723312b4b73ebc283b5792aa3e9b999ae463846b","observation_id":"d5d1109f-bb91-4e8f-b360-8938d288d51f","resolution":{"observed_at":"2026-08-11T22:39:03.366484Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:06.736417Z","title":"Diffusion models in bioinformatics and computational biology","venue":null,"work_id":"3ddd1f8c-4c47-4f87-926c-0141b761a063","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.395196Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:9d2d7470c52f0605f46e18c9d44b5eb35fc9a3d9e5b0efabe4fcc52b022035eb","observation_id":"2fdec0f6-c2f2-4994-826a-3c8eba684e6c","resolution":{"observed_at":"2026-08-11T22:39:06.774744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:39:03.407531Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.407531Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:6f866d19c5b956868f6769e6b4ffb81ddfa5f5160ab17cfe74d5ce2768a63fb5","observation_id":"4a60eb25-8bed-4b0d-a27c-58ac9b75afd5","resolution":{"observed_at":"2026-08-11T22:39:03.407531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12598","last_updated":"2022-07-26T01:42:07Z","snapshot_observed_at":"2026-08-14T06:37:15.299690Z","submitted_at":"2022-07-26T01:42:07Z","title":"Classifier-Free Diffusion Guidance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12598","snapshot_observed_at":"2026-08-11T22:39:03.415516Z","title":"Classifier-free diffusion guidance","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.415516Z"},"links":{"cited_paper":"/paper/2207.12598","citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:8a8a8431acb396698365cec392a6c871f192ba562ffdafe22153a78d54ac39ac","observation_id":"3c2a8978-ff26-4293-b0ee-00203188754b","resolution":{"observed_at":"2026-08-11T22:39:03.415516Z","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":"2026-08-11T22:39:03.423351Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.423351Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:58d4863b1513d2e1a9adeba03ed20f018b1be4225fc4d35771d3712f4d65ceae","observation_id":"3d731e59-1c90-4005-bdeb-825b886b219f","resolution":{"observed_at":"2026-08-11T22:39:03.423351Z","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":"2026-08-11T22:39:03.430773Z","title":"Highly accurate protein structure prediction with alphafold","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.430773Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:dd108fc4cb1fc883cd7f831eaecc6ec264be0a002fd3f7e5ec45825434aec3a9","observation_id":"b04b83f8-7ee5-442e-ac11-3f0278372ef0","resolution":{"observed_at":"2026-08-11T22:39:03.430773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06079","last_updated":"2024-04-17T16:31:33Z","snapshot_observed_at":"2026-08-16T14:20:03.363503Z","submitted_at":"2024-02-08T22:06:55Z","title":"DiscDiff: Latent Diffusion Model for DNA Sequence Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06079","snapshot_observed_at":"2026-08-11T22:39:03.438122Z","title":"Discdiff: Latent diffusion model for dna sequence generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.438122Z"},"links":{"cited_paper":"/paper/2402.06079","citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:0357b3607633d1155816b3ff468c138ed39416f95b3659e0696d5b1139799ae9","observation_id":"396be6e3-5954-4b8c-9ef0-6951b066f307","resolution":{"observed_at":"2026-08-11T22:39:03.438122Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:06.636114Z","title":"Predicting the prevalence of complex genetic diseases from individual genotype profiles using capsule networks","venue":null,"work_id":"8efb807e-b6ed-4043-b95b-b1a68c7aa83a","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.452091Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:9fd43e650eda4b03eccebd9c165abf1f2381353264bb4db6a0788a1cf46b04f1","observation_id":"1aa33907-673f-4def-876c-a3c326ccf08a","resolution":{"observed_at":"2026-08-11T22:39:06.644743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:39:03.455578Z","title":"Umap: Uniform manifold approximation and projection","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.455578Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:7f36df65b72e347b0074e1da6a8283d04609806fb0cef048adcd6f55b63f842b","observation_id":"98140148-f0c1-4ead-8c76-77fda2393b13","resolution":{"observed_at":"2026-08-11T22:39:03.455578Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:06.538559Z","title":"Baccus, and Chris Ré","venue":null,"work_id":"15d0dc69-f344-4529-8c1f-c13d7332f79a","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.584766Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:28317ceb1c8cd5cafa16f44d60f6df22f920cb65ec98cfd258245a23724946db","observation_id":"20203ed1-7748-4b6a-9bf0-9bdfbf1e48b8","resolution":{"observed_at":"2026-08-11T22:39:06.584762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:06.368348Z","title":"Generative moment matching networks for genotype simulation","venue":null,"work_id":"ebbea3c3-8e2e-4007-93a7-0ff78198b1bb","year":2022},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.679270Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:674b2f6d00237dc58e11cb0b689c7491511927c0c24963d7a3e1d86ccd1d7f06","observation_id":"d1d40159-36ca-4b58-8b90-f30e7dc27796","resolution":{"observed_at":"2026-08-11T22:39:06.434758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:39:03.691170Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.691170Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:9ec3833f1938b87457ff7fcba300aae1645263a61e90c689c3f2256b9930c047","observation_id":"6972c2da-b007-4714-951f-c1e3e7d07653","resolution":{"observed_at":"2026-08-11T22:39:03.691170Z","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":"2026-08-11T22:39:03.699944Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.699944Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:9e3d319da7522d09c32ea5906809c99df56d01574411899b73deff5a39d80cd2","observation_id":"1dff94bf-116f-4070-b305-325878385d4e","resolution":{"observed_at":"2026-08-11T22:39:03.699944Z","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":"2026-08-11T22:39:03.715230Z","title":"Improved techniques for training gans","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.715230Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:4c53b3dacee4b36b0e4ae9ca616c5cd7266a6ad743681772906cfb593608a304","observation_id":"4e545ee5-eef7-4d76-9484-6c9e439ee51f","resolution":{"observed_at":"2026-08-11T22:39:03.715230Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:06.246919Z","title":"Designing dna with tunable regulatory activity using discrete diffusion","venue":null,"work_id":"6878124d-9679-48d3-ab4e-5d00e25dd24c","year":2024},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.764756Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:e65b68360f35199c91a589c4f280b7277fd564e7fc34acdebf82e60676ca9eb0","observation_id":"d9ab5e79-ec67-461b-a7b5-22ea5f68d10a","resolution":{"observed_at":"2026-08-11T22:39:06.274842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03234","last_updated":"2024-06-05T21:02:37Z","snapshot_observed_at":"2026-08-19T15:11:40.646454Z","submitted_at":"2024-03-05T01:42:51Z","title":"Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03234","snapshot_observed_at":"2026-08-11T22:39:03.767998Z","title":"Caduceus: Bi-directional equivariant long-range dna sequence modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.767998Z"},"links":{"cited_paper":"/paper/2403.03234","citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:d14fc87b89e962fec5646067a681ba699dfaca391f303899885836802b552f81","observation_id":"b9d6b1a7-7ced-4892-826a-0397b254b6b8","resolution":{"observed_at":"2026-08-11T22:39:03.767998Z","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":"2026-08-11T22:39:03.773850Z","title":"Dna-diffusion: Leveraging generative models for controlling chromatin accessibility and gene expression via synthetic regulatory elements","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.773850Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:6697aa9c0200f985d54895f51ea181c2b96b5dbeeb587fcd64276e03ebc435d7","observation_id":"8c34b685-36ba-4fd8-87d6-33144c7e7868","resolution":{"observed_at":"2026-08-11T22:39:03.773850Z","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":"2026-08-11T22:39:03.783243Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.783243Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:ebce0de501aac8a7a7506de7863099ebcc1ae6ad7f0a4753b9d8d584f90ebec0","observation_id":"510ae02c-fdb9-45f6-b3de-82dd8f18683f","resolution":{"observed_at":"2026-08-11T22:39:03.783243Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:06.107760Z","title":"Understanding and mitigating copying in diffusion models","venue":null,"work_id":"efa3c0f2-a722-4a5a-9742-6ca3babbf1f1","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.794087Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:8f4e4e2b548e7722e11fdb7fa94ee759c33a81e9edddfc093d15402ca2fed7d0","observation_id":"00b29b96-f751-4c18-a82e-94656b94aa86","resolution":{"observed_at":"2026-08-11T22:39:06.141972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:39:03.800003Z","title":"Denoising diffusion implicit models, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.800003Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:143a802d0636e0cc5477485ba141bfc58a121ab53707a155fc71cbcbc5b6c645","observation_id":"3b4c9939-0953-4c3a-ad4c-f7408f04e68c","resolution":{"observed_at":"2026-08-11T22:39:03.800003Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:05.949854Z","title":"Towards creating longer genetic sequences with gans: Generation in principal component space","venue":null,"work_id":"26fcc068-7fab-46f8-9919-27e8f8d40895","year":2024},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.804182Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:ab9b2acbbaa7ad89fa4c7c0c10e72851c329e5aa69ffbee32b4f10e1f0b665de","observation_id":"928238e8-7337-4fae-a05d-5c08648deedc","resolution":{"observed_at":"2026-08-11T22:39:05.978730Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:39:03.813311Z","title":"Visualizing data using t-sne","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.813311Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:da5f057e00994ec7ad42cf249407a95cced36dc56558ca2b240a7952849773e8","observation_id":"3d6f9db4-d10c-45f4-813d-926d40511ce4","resolution":{"observed_at":"2026-08-11T22:39:03.813311Z","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/bioinformatics/btad535","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:04.236403Z","title":"HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes","venue":null,"work_id":"247411ac-bffe-4aaf-bfc0-c9de4823f576","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.821172Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:63cb9709fa16fa4a1dbf3f89da25efbec6ab38dbfca04008376b85b96f4d3233","observation_id":"c53375c9-5eff-4a33-952c-0b3ea0a8861c","resolution":{"observed_at":"2026-08-11T22:39:04.254583Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:05.604761Z","title":"Discovery of a structural class of antibiotics with explainable deep learning","venue":null,"work_id":"979f1984-4dff-4e72-81d1-9649c79e6f46","year":2024},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.853716Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:c6482e185d52f069c75c128bf5e68753ce5fbb564d27b147378af78bf2659662","observation_id":"f22ee2ea-3b6b-47a3-b54d-b3612bb34dd0","resolution":{"observed_at":"2026-08-11T22:39:05.644662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:05.273466Z","title":"Privacy preserving synthetic health data","venue":null,"work_id":"a157f665-5be3-405d-8872-cb9b7811972e","year":2019},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:03.972532Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:6e6782cff3c10983bd0450d6dea2db940d3294c3cf98c1f0dd0532f142f6e9f4","observation_id":"447ea457-2121-4edb-b8b8-aa870e7771bf","resolution":{"observed_at":"2026-08-11T22:39:05.424760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:05.150358Z","title":"Creating artificial human genomes using generative neural networks","venue":null,"work_id":"2cfe7de7-6d40-4e96-a2d0-0f8ffb260cb4","year":2021},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:04.124754Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:86ae58a6c0a74f716f94c86e2ed49953bea3d5f615e96db0686e839d42f2301a","observation_id":"bdf044bb-14d6-438c-aafd-22b7b1a3eb3e","resolution":{"observed_at":"2026-08-11T22:39:05.184750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:04.796265Z","title":"Deep convolutional and conditional neural networks for large-scale genomic data generation","venue":null,"work_id":"cb34fec4-a501-4028-b4f2-3dc925f01d91","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:04.149712Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:2cb90928ed8e4d661897ed3a13b2c9908c43b00cb9b6195ff523dd7d46ff1aad","observation_id":"3a7638c4-a9df-4536-bbf1-4258f689c680","resolution":{"observed_at":"2026-08-11T22:39:04.956331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:04.746923Z","title":"Dnagpt: a generalized pretrained tool for multiple dna sequence analysis tasks","venue":null,"work_id":"1c9af2fc-68fb-41c5-a678-6750b3458707","year":2023},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:04.161315Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:88faab632fe3f1fdebe6d6f18fd4fe46fa20860bb4a7a886b6d4055ec36e3827","observation_id":"46b7485a-2485-4056-955f-7669b7ae884b","resolution":{"observed_at":"2026-08-11T22:39:04.767823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-11T22:39:04.171446Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:04.171446Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:ba731e48e96766e3d5339d758f777133183306c6691f412fe47d1635d8dc591a","observation_id":"bf49a572-21ac-46a0-b35f-1d2e4bf6e86a","resolution":{"observed_at":"2026-08-11T22:39:04.171446Z","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":"2026-08-11T22:39:04.189501Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:04.189501Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:f573ddef420322dc3877c8d127b78158fc3e301f2cd8e01c15df820e1f19c2b9","observation_id":"29825937-d690-4a68-b7da-46ab8e0e78d0","resolution":{"observed_at":"2026-08-11T22:39:04.189501Z","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":"2026-08-11T22:39:04.200388Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:04.200388Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:4d501ccaed887de5da27a62fdf77645c2ce41990dbee9d323dcdf43791f38878","observation_id":"5a390c4d-d3ad-4ccf-a9a1-77a6758ac900","resolution":{"observed_at":"2026-08-11T22:39:04.200388Z","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":"2022.87771","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T22:39:04.460189Z","title":"oup-authoring-template.cls","venue":null,"work_id":"889615aa-66ab-479f-8551-a27fec4caa58","year":2022},"citing_paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models","version":3},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-11T22:39:04.206386Z"},"links":{"citing_paper":"/paper/2412.03278"},"observation_digest":"sha256:222fef79fd332a40c4d10ebbde3fd73d857ff0511e866d72ed6fd7395c2f298f","observation_id":"ae325231-1f86-4ec3-a73c-86fca608c8fe","resolution":{"observed_at":"2026-08-11T22:39:04.517946Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.03278","last_updated":"2025-01-30T12:14:35Z","latest_version":3,"primary_category":"cs.CE","snapshot_observed_at":"2026-08-19T15:11:54.250092Z","submitted_at":"2024-12-04T12:37:59Z","title":"Generating Synthetic Genotypes using Diffusion Models"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":21,"verified_exact":1,"verified_fuzzy":21},"total_outbound_references":44},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2412.03278."}