{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002150262,0.0000589705,0.00008101024,0.00004337801,0.00003780146,0.000009793502,0.0000553385,0.00007141355,0.000003481864],"category_scores_gemma":[0.00004395834,0.00004093491,0.00002324035,0.00003104534,0.00005310814,0.000003460879,0.00003324633,0.00003590111,0.0000011587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001294344,"about_ca_system_score_gemma":0.00003457066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.612443e-7,"about_ca_topic_score_gemma":0.000002879744,"domain_scores_codex":[0.9995993,0.00005177624,0.0001310632,0.0001115009,0.00003745073,0.00006895953],"domain_scores_gemma":[0.9996256,0.00004017277,0.0001197857,0.00003623113,0.0001245929,0.00005360281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001664513,0.00001540659,0.003215671,0.000007219781,0.00007439246,5.144842e-7,0.00005427656,0.001009844,0.9421837,0.00314684,0.0002689223,0.04985676],"study_design_scores_gemma":[0.005954228,0.006284633,0.4694147,0.0001335351,0.0001740819,0.003368231,0.000342042,0.006229639,0.1149062,0.3430422,0.0490091,0.001141412],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6209273,0.0002406964,0.3779938,0.0007273361,0.00004639478,0.00004378159,0.000002746853,0.00000109886,0.00001678562],"genre_scores_gemma":[0.9904981,0.00009616779,0.008964057,0.0002443142,0.000138828,0.000002460248,0.000008105324,0.000005065933,0.00004291604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8272775,"threshold_uncertainty_score":0.1669277,"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."}}