{"as_of":"2026-08-14T16:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fa0e0389d3a4e64e91d4a1f2a442921568c7acc0662de88e3c1abd745df76c3c","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T23:56:59.195604Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2606.06730/citation-record","integrity":"/paper/2606.06730/integrity","json":"/paper/2606.06730/citation-record.json","paper":"/paper/2606.06730"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-27T23:56:59.195604Z","title":"Rank Discrim- inants for Predicting Phenotypes from RNA Expression.The Annals of Applied Statistics, 8(3):1469–1491, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:5fff878620d6ab0efd5ae4734574670f2a86bab1830e953ed930b573a8f4b6b1","observation_id":"283532dc-1359-472c-b566-63f8020ffbb0","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Deciphering signatures of mutational processes operative in human cancer.Cell reports, 3(1):246–259, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:9c41f68702d1c8144b78d5196c2ba0af92885b6cf9d9a1a65ae47475b945b692","observation_id":"d7d176df-ce05-4940-8f4f-44e25ee1ab69","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"A tutorial on adaptive mcmc","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:0dd0db2540b28c6770b27b981a215fcdad8d79ac2cbb2f454672224d4c31d231","observation_id":"90906d3b-4aa1-4a93-8841-9e43579d00c5","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:8e4e6d39c901ded7be0ca4ec9eedd6242fa670724b32b0a345073b430d8690d9","observation_id":"625b65f0-db6f-42ee-9d9e-ab9656eec252","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Integrative clustering reveals a novel split in the lumi- nal A subtype of breast cancer with impact on outcome.Breast Cancer Research, 19(1):44, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:5485e2c996d49b3852fb2eda73cbd02ea831fcffb059ad49fe58c324e71f6735","observation_id":"3a371b89-f9b1-427c-9c73-50b66855e05f","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Badgeley, Stuart C","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:a60368826aba29f1adef43ded71e4f320ffcad5c8523183e3434067b26560a69","observation_id":"aa98a250-6990-436c-b4ff-0c9daa8e3ee7","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2506.16295","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-02T15:17:08.235377Z","title":"Understanding uncertainty in bayesian cluster analysis.arXiv preprint arXiv:2506.16295","venue":null,"work_id":"e00d1e5d-8d04-4ae0-bc25-b10bd07004b3","year":2025},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:ca50ceb31af90bef30bb1e2a92693b29e454afe02b9d1e3ad020edb707990622","observation_id":"0c56a420-9fc9-471e-a804-f6193a0abccf","resolution":{"observed_at":"2026-07-02T15:17:08.238174Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-27T23:56:59.195604Z","title":"PAM50 breast cancer subtyping 34 by RT-qPCR and concordance with standard clinical molecular markers","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:bbccf72cd386475a355bc1e93ffae00e122d6a7a000aff762dfc5840f8d2ea67","observation_id":"bce06cd6-35e7-4d6e-b550-81da179f52cb","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.11780","last_updated":"2026-07-07T11:21:52Z","snapshot_observed_at":"2026-08-14T07:01:51.304648Z","submitted_at":"2024-08-21T17:09:54Z","title":"Adaptive Stereographic MCMC","version":3},"cited_work":{"arxiv_id":"2408.11780","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.11780","snapshot_observed_at":"2026-07-08T01:18:17.324447Z","title":"Adaptive stereographic mcmc","venue":null,"work_id":"bb8cf5ac-2584-40cc-958b-32a5087c441f","year":2024},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"cited_paper":"/paper/2408.11780","citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:658582c3574f98dbb437a96b334093eddf67aad0ffc475b90fd9fe3b713fedbc","observation_id":"896edc59-29b9-431f-990a-82bc68cd104a","resolution":{"observed_at":"2026-07-08T01:18:17.324447Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-27T23:56:59.195604Z","title":"Chen and Daniela M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:e746c6abb71b9eacd6b79c27e2df15e3f11a369b762702cdf38f6d8b541454f2","observation_id":"62253472-b3f4-48eb-b31a-d68829d5e9bc","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Stability post-processing for items im- portance in preference learning via the bayesian mallows model","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:8471bfa506c13cf4b2b6e2fd30586ead738bf907e442b4440898d99a658b1830","observation_id":"126f1a56-ae3b-4f93-ad44-0553e78a6715","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"The genomic and transcriptomic ar- chitecture of 2,000 breast tumours reveals novel subgroups.Nature, 486(7403):346–352, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:f1da04e4014daa41b4cb8bab69241c910ba1d8700ed1b1a09d4722547a6c5c92","observation_id":"55378100-1be2-4478-8546-999739167cff","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Bayesian aggregation of order-based rank data.Journal of the American Statistical Association, 109(507):1023–1039, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:20e956eca23e5d0195144958670f6819e070139453c15c3a78aa3100ecce30ea","observation_id":"3266ade3-6112-43c3-aefe-723941d62b22","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Vol- ume 11 of Lecture Notes - Monograph Series","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:fa74102809201919b6688e240f14a9884defec5fba546dfcf2505d73d5e638f7","observation_id":"06f869bb-52b9-4af6-a9a6-b1e871b2aa9d","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Rank-based bayesian variable selection for genome-wide transcriptomic analyses.Statis- tics in Medicine, 41(23):4532–4553, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:d5c124fec8454a90ab62efe7cbfe17085b3b44c13343c944ea282c3e915d07de","observation_id":"af01b0f2-95a8-4983-bbd1-24b13a5e1200","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Systematic bias in genomic classifi- cation due to contaminating non-neoplastic tissue in breast tumor samples","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:bcddab65f3bc599831c0af4eb422eb73b7d312ae5a8c14986c830f51c7c70a03","observation_id":"00021bb7-9a9f-4194-836b-dba409abc3ce","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Dna methylation at enhancers identifies distinct breast cancer lineages.Nature Communications, 8(1):1379, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:091cf043c5e0d45358f4a68eea18ba1cc7fd0a39b13d3130d6aedcae074d0aa1","observation_id":"a1484a0f-dc66-4bd1-a15d-03e8d16d939e","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":null,"venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:7b0b9b01217a389b642fc1cc943f6a13b145d52adf03d27f64747c0c9513e717","observation_id":"4df3cf59-cedd-4629-a19c-58afde0bc535","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Variable selection methods for model-based clustering.Statistics Surveys, 12:18–65, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:0b1fe99b6e95c0f990ba7caa6c36930472f3271390c4ef240d746770c59962c2","observation_id":"9c7d89f7-a807-4656-bb4b-91876c46f9e4","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Selective inference for hierarchical clustering.Journal of the American Statistical Association, 119(545):332–342, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:24bfdf1bca67ef666a0401994d8b0d7becc3a2251e34d3c5b66f03a7cffedbd6","observation_id":"a09c1c47-01ee-4264-96b1-0f4709db125d","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Uncovering clinically relevant breast cancer subtypes biomarkers using integrative bioinformatics and machine learning approaches.Biomarkers, pages 1–12, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:2032204f46aa9c3a1033082b23aa02c366642b17502b8181611c461a7c03ff26","observation_id":"d78c3b06-4380-42c5-91fc-286502932d56","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:2c5b9c80fe6346b77cb3e2a4148a3d565692c4011089d8c287075a5ba4900450","observation_id":"ed6aa4b5-b973-4022-86d7-4d5db60fdbf8","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome.BMC Bioinformatics, 12(1):323, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:4e1dce9fdddd133267d038b144f69cdba3ed86cc18a9bdd4b534c11983557608","observation_id":"3eac83ef-00e6-4750-a607-f47481f19c13","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"A sparse negative binomial mixture model for clustering RNA-seq count data.Biostatistics, 24(1):68–84, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:974674207fc85c49f396f2775abaf909ffbfe0626f1e236b72c496e4bf4f5731","observation_id":"f7e6929e-ceab-4d98-a07f-302426aac9df","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Model-based learning from preference data.Annual Review of Statistics and Its Application, 6(1):329–354, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:f8ce939a30f69829d7635d48b048b4407957d4b7c75154926a67279b3774222e","observation_id":"40e208e8-7542-42d8-8640-71199a9b39c9","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Effective Sampling and Learning for Mal- lows Models with Pairwise-Preference Data.Journal of Machine Learning Research, 15(117):3963–4009, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:01bc2ebb6d86cce9f0cef650c2cee24be0d95fcaaef9c0d70c24a45b3e417982","observation_id":"c6fc00da-bdc8-4f06-a2cf-0a7cb323a9cb","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Luce.Individual choice behavior: A theoretical analysis","venue":null,"work_id":null,"year":1959},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:a67ea6aa570a83b1d6c9d89603640c01e86cfa85332ddb1784b5fc398500f11d","observation_id":"27a1218a-ccdc-4754-bab7-414f12cff6d1","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Non-null ranking models","venue":null,"work_id":null,"year":1957},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:1dc9f54d092cca5dfd9153370507759b6836c3d6122c82f40b550b68cb3458d5","observation_id":"9398ad8a-a55b-4789-8500-a284c95beb4e","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"An Exponential Model for Infinite Rankings","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:3ae4381d33da80a4dd9e91618aa086624689f519881b63d3e8528d591552171d","observation_id":"0f799ff3-78e1-473f-ac2b-099042c4d30b","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Dirichlet Process Mixtures of Generalized Mallows Models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:68ea2985e1f41c28ad1dffa5b4c27898f1eef9f104d8abde14911987c72bd86c","observation_id":"96be0a7b-f384-46c3-a467-bfef775561af","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"A fully bayesian latent variable model for integrative clustering analysis of multi-type omics data.Biostatistics, 19(1):71–86, 05 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:e28d559548debaf897e69497cd47884121fc002232e463335f2ae851490b5330","observation_id":"3f31b9ea-6116-4ea4-a52f-67151dd6c99f","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"PGC-1 alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human di- abetes.Nature Genetics, 34:267–73, 2003","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:ee50d9a03424acc2f87be6077dab4739d2650be941497c29fcb280c09cad2773","observation_id":"c004856f-f2ab-4889-81d6-796ec81d9a02","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Spike-and-slab lasso biclustering.The Annals of Applied Statistics, 15(1):148–173, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:5b5163731d9111257eba8d0335f6910ddf33e566e0ea5aac065193a8aeaa530a","observation_id":"1ebae1c7-a5ff-4735-86cb-9ae7864cad6e","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Comparison of sparse biclustering algorithms for gene expression datasets.Briefings in bioinformatics, 22(6):bbab140, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:20bc005381702e9019aadbae08c1ebfd6dfd6a8b7407b64f9e207296a6240776","observation_id":"28155c6b-a263-49a7-82ee-5f0655ca8ce1","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Parker, Michael Mullins, Maggie Chon U Cheang, Samuel C Y Le- ung, David Voduc, Tammi L","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:7964351c639ab7e820efc0e8ac8bb1a43824c04c7213b461a0c976904da11428","observation_id":"d1720752-bc03-4afe-aadd-5fac89cf9c58","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Molecular portraits of human breast tumours.Nature, 406(6797):747–752, 2000","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:3eea87f980a69506f8c9275859f3e754ee16d1892ce4a103f09f9ecf33f09fb3","observation_id":"781691b3-2e95-4b74-9855-38d4ac8aed10","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Plackett","venue":null,"work_id":null,"year":1975},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:98320df468eaedc8908f6d90f4a73792b9186cb3db49f236b35f0423e85d2668","observation_id":"f1682df2-502b-4d22-a112-8615fcce3fea","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Clinical implications of the intrinsic molecular subtypes of breast cancer.The Breast, 24:S26–S35, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:f6065c37a6ec9d87449500e2b29a76741cb407f7b37f115fd87e2d22520eca49","observation_id":"77ef6db7-b057-4437-930a-44bc8e1f7faf","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Network-based prioritization of cancer genes by integrative ranks from multi-omics data.Computers in Biology and Medicine, 119:103692, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:1a7436962cdf8983225402eecfba558cd9f0853e0124d2e8c9e674321e7da07f","observation_id":"c9b387ec-d7bb-4a87-b976-bfe2e549ae58","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"BayesMallows: An R Package for the Bayesian Mallows Model.The R Journal, 12(1):324–342, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:0a411801a000ce0be0d7ece995ec3d789b0ad742983ac23a88254334cd6c97e4","observation_id":"6232546c-20bc-4233-a30d-c06c0addf879","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Mootha, Sayan Mukher- jee, Benjamin L","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:6fdd36a6d8e7dddaf3b54ce52be991a5648b9d115f2cdbf7cd26f35a761432f4","observation_id":"9e5e93ab-a2ad-44d5-8419-1937656bb63f","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Perou, Robert Tibshirani, Turid Aas, Stephanie Geisler, Hilde Johnsen, Trevor Hastie, Michael B","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:b3444cfa7844734f923a5bae2470500b0294b167fd0bbba2dc6993e49ef4df1e","observation_id":"cad1f610-2165-4223-93a5-235739cf3d98","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Bayesian variable selection in clustering high-dimensional data.Journal of the American Statistical Association, 100(470):602–617, 2005","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:cd31ced440974391b08e65c0a80efbd9deee28c98df5ef15650f365b0cde6a0c","observation_id":"1a821c51-d762-43c5-8d84-44f8d0c32232","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":null,"venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:065dca3b079adf02122df9dc3bac9dd7c6ce4f7cfa6b9bb288f2b6922c4120d2","observation_id":"b160a426-e97e-4de1-821c-c8ac758c1376","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Probabilistic preference learning with the mallows rank model","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:afdcb9ee8fa40f6b70d8e2aa1a5b8d5145fb54130b9703bc9eeae7f2ddb55b83","observation_id":"44b5153b-26e9-49e8-acfa-31a3280d0fbc","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Bayesian Cluster Analysis: Point Esti- mation and Credible Balls (with Discussion).Bayesian Analysis, 13(2):559– 626, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:4ff0c95a7cf44f1977bfd233ac9a7842e5e2c6d62d543cd156823ae7193a3abd","observation_id":"eba2e457-5c44-4c1c-9a16-d44ab4549df9","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery.Nucleic Acids Research, 38(18):e178, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:0456bf5fd864fc4ecbde33b7d6170aa5b4ea0746e3c11b0cc311f86f4fe4d708","observation_id":"83e5db94-970e-4e19-a50c-0c1964fb2b23","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Breast cancer molecular profiling with single sample predictors: a retrospective analysis","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:f9fd59d8ad077bee22ee8c6a2dbd0e88a5ae93126b7ceda099f3a9e9fec72d80","observation_id":"cb09be66-39fe-455f-9439-d95a84ccaec2","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":"Witten and Robert Tibshirani","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:d4cf6b58fa99bf9bf63faa06e87dc23136c01d773211c4fef1a76c491d91dfd5","observation_id":"a2d4defc-1364-4d2c-a457-86a88b641d00","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","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":"2026-06-27T23:56:59.195604Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-27T23:56:59.195604Z"},"links":{"citing_paper":"/paper/2606.06730"},"observation_digest":"sha256:ca4ca7f8d365a484fc53735b1ac9af78895e7a4902afe341bcc608ed8e90ecde","observation_id":"3f66045d-25c8-4860-9e1c-d1dfbde5ea8b","resolution":{"observed_at":"2026-06-27T23:56:59.195604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.06730","last_updated":"2026-06-04T21:30:13Z","latest_version":1,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-09T18:51:55.121908Z","submitted_at":"2026-06-04T21:30:13Z","title":"Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":48,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":50},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2606.06730."}