{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002685882,0.002057323,0.00194388,0.005653303,0.001214724,0.002418847,0.002533281,0.001470748,0.01118325],"category_scores_gemma":[0.0105458,0.0009820875,0.004054663,0.00396078,0.0005619603,0.001382013,0.003986817,0.001568135,0.004594686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001414594,"about_ca_system_score_gemma":0.003296669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005056536,"about_ca_topic_score_gemma":0.009022052,"domain_scores_codex":[0.9986067,0.0002346413,0.000139561,0.0004783552,0.0003759096,0.0001648374],"domain_scores_gemma":[0.9973866,0.0008327343,0.0001629286,0.0007631305,0.0006520532,0.0002025829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00221608,0.0004936653,0.06982797,0.001204738,0.001324402,0.001623538,0.0009487222,0.05767333,0.01387609,0.02857128,0.1739933,0.6482469],"study_design_scores_gemma":[0.0006281799,0.000358175,0.01179819,0.0002041472,0.000453062,0.002906172,0.0006185818,0.7763526,0.02183085,0.09356283,0.09113067,0.0001564685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06245772,0.001031146,0.8491723,0.0007689664,0.0004659492,0.001608926,0.02400236,0.05121245,0.009280156],"genre_scores_gemma":[0.1532414,0.0005776352,0.751702,0.0006073733,0.0001497463,0.001509826,0.07971756,0.007629874,0.004864678],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01118325,"threshold_uncertainty_score":0.03741175,"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."}}