{"id":"W3163183329","doi":"10.1101/2021.05.13.444087","title":"iSubGen: Integrative Subtype Generation by Pairwise Similarity Assessment","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"Canadian Institutes of Health Research; University of Toronto; National Cancer Institute; National Science Foundation; National Institutes of Health; Prostate Cancer Canada","keywords":"Subtyping; Data type; Similarity (geometry); Computational biology; Computer science; Pairwise comparison; Metric (unit); Data mining; Feature (linguistics); Signature (topology); Bioinformatics; Artificial intelligence; Biology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004459105,0.001862329,0.001993191,0.007695888,0.001962381,0.003933193,0.003967633,0.001703817,0.014855],"category_scores_gemma":[0.01803111,0.001105517,0.003813351,0.005892306,0.0008362318,0.002663134,0.004414864,0.002342672,0.00863674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001589205,"about_ca_system_score_gemma":0.00359023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006833702,"about_ca_topic_score_gemma":0.009815075,"domain_scores_codex":[0.9960801,0.0008153787,0.0003227819,0.001022879,0.001420118,0.0003388104],"domain_scores_gemma":[0.9946198,0.001603778,0.0003453356,0.001804502,0.001266014,0.0003604512],"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":[0.00148083,0.0005308939,0.02796895,0.0007709991,0.0007205302,0.0006421152,0.0006647531,0.03099698,0.009210424,0.02335408,0.2249397,0.6787198],"study_design_scores_gemma":[0.0004543896,0.0002785013,0.006697181,0.0001861245,0.0002348246,0.001105373,0.0006757407,0.7846341,0.01727583,0.1044373,0.08387213,0.0001484321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02418921,0.0006847089,0.8836119,0.0006391585,0.0004042135,0.00109161,0.01455416,0.06989086,0.004934079],"genre_scores_gemma":[0.0858409,0.0002927567,0.8486564,0.0003835676,0.0001559599,0.0008743968,0.05095699,0.008474564,0.004364396],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.014855,"threshold_uncertainty_score":0.0496949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01680894749507946,"score_gpt":0.2571526080704576,"score_spread":0.2403436605753782,"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."}}