{"id":"W2794553059","doi":"","title":"CREAM: new feature selection approach for epigenomic profiles of cells","year":2018,"lang":"en","type":"article","venue":"Research in Computational Molecular Biology","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Epigenomics; Computer science; Feature selection; Selection (genetic algorithm); Feature (linguistics); Artificial intelligence; Chemistry; DNA methylation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001012068,0.00007610553,0.0001232433,0.0003546453,0.00009550385,0.00003475196,0.0005010422,0.000106233,0.000004027417],"category_scores_gemma":[0.0001590551,0.00007181693,0.0000359718,0.0006286022,0.0001431238,0.00006557744,0.0001307573,0.0002107447,0.00001165186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006022775,"about_ca_system_score_gemma":0.0003556956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001090166,"about_ca_topic_score_gemma":0.000004602645,"domain_scores_codex":[0.9985446,0.0004159436,0.0001753814,0.000405638,0.0001900091,0.0002684737],"domain_scores_gemma":[0.9989743,0.0003282235,0.00007160388,0.0002033513,0.000361233,0.00006123976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009959377,0.0002797634,0.002186663,0.00009673714,0.00003500127,0.000001257194,0.0002241289,0.01835744,0.2004019,0.7264879,0.003282413,0.04854722],"study_design_scores_gemma":[0.0005251777,0.000803529,0.004579793,0.00001301051,0.000001719518,0.000007905074,0.0000122498,0.8246678,0.02114475,0.1443096,0.003811328,0.0001230843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008873304,0.00009461246,0.9883721,0.001631161,0.00005553896,0.0003656698,0.000009701624,0.00002538999,0.0005724971],"genre_scores_gemma":[0.5813724,0.000002647469,0.4182741,0.00003514867,0.0000798808,0.00002734019,0.0001358488,0.000005092934,0.0000675988],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8063104,"threshold_uncertainty_score":0.292861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06525411104604177,"score_gpt":0.4057354812409324,"score_spread":0.3404813701948907,"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."}}