{"id":"W3108863570","doi":"10.1101/2020.11.30.404038","title":"Balanced Functional Module Detection in Genomic Data","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","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; Computer science; Feature selection; Outcome (game theory); Property (philosophy); Variable (mathematics); Set (abstract data type); Consistency (knowledge bases); Data mining; Artificial intelligence; 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":[0.003263045,0.0006996442,0.000712952,0.003582264,0.0004509316,0.001029422,0.001055577,0.0007679862,0.001008672],"category_scores_gemma":[0.01375214,0.0003623108,0.0008388406,0.00213999,0.0006749858,0.001092223,0.001288017,0.0007459863,0.0002813745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006996731,"about_ca_system_score_gemma":0.0006284602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001870935,"about_ca_topic_score_gemma":0.001672121,"domain_scores_codex":[0.9985741,0.0006194208,0.00006583436,0.0003614844,0.0002756199,0.0001035978],"domain_scores_gemma":[0.9926566,0.005168726,0.0007903697,0.0005162085,0.0006799431,0.0001881056],"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":[0.0007709205,0.0002367121,0.09570139,0.0007201816,0.0006506978,0.001004805,0.0005878251,0.4513748,0.05991357,0.03506019,0.007367514,0.3466113],"study_design_scores_gemma":[0.00002010416,0.00004530741,0.005233806,0.00001977014,0.0000312347,0.0001336811,0.00004363141,0.9500234,0.007429109,0.0354379,0.001564096,0.00001789114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1458278,0.0003181408,0.8499796,0.0002586065,0.00002373388,0.00008723463,0.001351744,0.001677317,0.0004757018],"genre_scores_gemma":[0.7035761,0.0001388072,0.2919114,0.0001812961,0.00003104937,0.0002084351,0.003209279,0.0001634603,0.0005801871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003582264,"threshold_uncertainty_score":0.0172568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01882974388236148,"score_gpt":0.2123224986053706,"score_spread":0.1934927547230091,"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."}}