{"id":"W2253609413","doi":"10.1089/cmb.2015.0189","title":"Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":207,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Genome Canada","keywords":"Artificial intelligence; Deep learning; Computer science; Feature selection; Feature (linguistics); Artificial neural network; Machine learning; Nonlinear system; Selection (genetic algorithm); Linear model; Pattern recognition (psychology)","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":[0.001133822,0.000756607,0.001004579,0.001380201,0.000313071,0.0006871111,0.0009073275,0.0009007053,0.001407251],"category_scores_gemma":[0.002708661,0.0003854151,0.0008698377,0.00152683,0.0006753358,0.0009634385,0.0009037098,0.001122941,0.0003064391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007032811,"about_ca_system_score_gemma":0.0007003306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002848326,"about_ca_topic_score_gemma":0.002042359,"domain_scores_codex":[0.999638,0.00008906578,0.00002710833,0.00008640029,0.0001121994,0.00004723306],"domain_scores_gemma":[0.9989302,0.0006678352,0.00009458644,0.00006175559,0.000207643,0.00003800282],"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":[0.0001343389,0.00009369535,0.00413013,0.0002133222,0.0001332232,0.0002009411,0.0001076957,0.4229197,0.0102504,0.0452656,0.006340086,0.5102108],"study_design_scores_gemma":[0.00001191326,0.0000284688,0.000543634,0.00001500472,0.00001970329,0.0000580501,0.000008532864,0.9737207,0.002068691,0.02184406,0.001670948,0.00001030731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007017225,0.0007504381,0.9909096,0.0002539177,0.000036651,0.00002298503,0.0001044882,0.0003261615,0.0005784294],"genre_scores_gemma":[0.5439833,0.002686671,0.4459836,0.0005360133,0.0002986634,0.0003593475,0.0009427592,0.0001603348,0.005049093],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002848326,"threshold_uncertainty_score":0.005996227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002544613022489751,"score_gpt":0.2572304031433438,"score_spread":0.254685790120854,"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."}}