{"id":"W1529356222","doi":"10.1002/path.4536","title":"Efficient molecular subtype classification of high‐grade serous ovarian cancer","year":2015,"lang":"en","type":"article","venue":"The Journal of Pathology","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Medical Research and Materiel Command; Cancer Council Western Australia; Cancer Council Tasmania; Cancer Council Victoria; Cancer Council South Australia; Peter MacCallum Foundation; Ovarian Cancer Australia; Cancer Council NSW; National Health and Medical Research Council; U.S. Department of Defense","keywords":"Serous fluid; Ovarian cancer; Biology; Serous ovarian cancer; Microarray; Computational biology; Gene expression profiling; Gene; Pathology; Cancer research; Bioinformatics; Cancer; Gene expression; Medicine; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005589066,0.00007324415,0.0001298556,0.00004476975,0.00001958834,0.000003057846,0.0001945037,0.00009433478,0.000008367965],"category_scores_gemma":[0.0001010898,0.00005179468,0.00005696296,0.00007577731,0.0000995129,0.000001408446,0.00004097787,0.0000784513,0.000002369298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002774423,"about_ca_system_score_gemma":0.0001762881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001872314,"about_ca_topic_score_gemma":0.000007767274,"domain_scores_codex":[0.9991579,0.0002463066,0.0002710336,0.00008038606,0.0001383985,0.0001059413],"domain_scores_gemma":[0.9989544,0.00001024421,0.000433054,0.0002369919,0.000303244,0.00006209287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001965874,0.0000621924,0.0006547748,0.000006322582,0.0000372095,0.00001521734,0.0001151966,0.01031484,0.9875056,0.0002585146,0.000401386,0.000432221],"study_design_scores_gemma":[0.001875801,0.0007114412,0.1029176,0.00003029163,0.000248796,0.001008013,0.0002359094,0.0007963672,0.8894725,0.001131574,0.00139668,0.0001749669],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889908,0.002022972,0.007973384,0.0006236151,0.0002469977,0.00007604712,0.00000775798,0.000001865901,0.00005658327],"genre_scores_gemma":[0.9991472,0.00005440971,0.0005354215,0.00007843998,0.0001400788,0.000001740718,0.000007792072,0.00001083087,0.00002412203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1022628,"threshold_uncertainty_score":0.2112126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02147748832479243,"score_gpt":0.2787674013742357,"score_spread":0.2572899130494433,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}