{"id":"W4403682865","doi":"10.1016/j.crmeth.2024.100884","title":"iSubGen generates integrative disease subtypes by pairwise similarity assessment","year":2024,"lang":"en","type":"article","venue":"Cell Reports Methods","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Terry Fox Research Institute; University of Toronto; National Cancer Institute; National Institutes of Health; Prostate Cancer Canada; Foundation for the National Institutes of Health","keywords":"Pairwise comparison; Similarity (geometry); Computational biology; Biology; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007303664,0.0002164033,0.0001822548,0.00003630996,0.00008421854,0.0001253274,0.0001007195,0.0001150032,0.00008794496],"category_scores_gemma":[0.0002476893,0.0001838706,0.0001629383,0.00009805012,0.00007940568,0.000004667698,0.0001339833,0.0001456322,0.000003497655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005434185,"about_ca_system_score_gemma":0.0004599584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003960709,"about_ca_topic_score_gemma":0.00001066164,"domain_scores_codex":[0.9985651,0.0001689497,0.0003082985,0.0006124611,0.0001171056,0.0002281093],"domain_scores_gemma":[0.9990286,0.00009365596,0.00008813778,0.0004819432,0.00009277458,0.0002148587],"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":[0.0000283658,0.0001273807,0.003132051,0.00009480017,0.00009516451,0.0003324455,0.00006816361,0.0001471791,0.8760185,0.000166201,0.09980704,0.01998268],"study_design_scores_gemma":[0.00006543694,0.00008398223,0.0005783533,0.00001718971,0.00008242872,0.00001491775,0.00003863159,0.0007715599,0.5210485,0.001397438,0.4756525,0.0002489844],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4562331,0.09341639,0.4281285,0.0007976817,0.00424023,0.0007285256,0.000292689,0.0001234025,0.01603943],"genre_scores_gemma":[0.8721413,0.003899811,0.111524,0.0007008214,0.0008244547,0.000129545,0.001035768,0.00008804006,0.009656238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4159082,"threshold_uncertainty_score":0.7498024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01180972854446835,"score_gpt":0.3488295207832329,"score_spread":0.3370197922387645,"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."}}