{"id":"W3201645418","doi":"10.1093/bioadv/vbab018","title":"Balanced Functional Module Detection in genomic data","year":2021,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Interpretability; Outcome (game theory); Property (philosophy); Computer science; Set (abstract data type); Consistency (knowledge bases); Variable (mathematics); Feature selection; Data mining; Artificial intelligence; Theoretical computer science; Machine learning; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00287147,0.0006925195,0.0006533646,0.00250713,0.000413815,0.0009664973,0.001168138,0.0006772401,0.001077227],"category_scores_gemma":[0.01348576,0.0002822829,0.0007536644,0.001834965,0.0007326192,0.001157125,0.001200623,0.0006628048,0.0002831568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007161457,"about_ca_system_score_gemma":0.0007242648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001539884,"about_ca_topic_score_gemma":0.001382547,"domain_scores_codex":[0.998696,0.0005385125,0.000074128,0.0003652942,0.000245992,0.00008003652],"domain_scores_gemma":[0.9927573,0.005439354,0.0006694795,0.000400209,0.0005641711,0.0001696245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000633514,0.0002200138,0.06881217,0.0008546255,0.0004063547,0.0009957245,0.0004672366,0.5496022,0.03367623,0.03894751,0.004567619,0.3008168],"study_design_scores_gemma":[0.00002211647,0.00007829147,0.005838377,0.00003585484,0.0000471454,0.0002375534,0.00005872804,0.9129856,0.00789231,0.07059115,0.002191219,0.00002170148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09933917,0.0003827587,0.896844,0.0002777314,0.00002511252,0.00009977612,0.001610933,0.0008755811,0.0005449195],"genre_scores_gemma":[0.6890998,0.0002908884,0.3047521,0.0002141347,0.00006061105,0.0002583938,0.004540132,0.0001122387,0.0006716647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00287147,"threshold_uncertainty_score":0.01518595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01469638284561246,"score_gpt":0.2381064015665066,"score_spread":0.2234100187208941,"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."}}