{"id":"W2602556590","doi":"10.1371/journal.pone.0174392","title":"ML2Motif—Reliable extraction of discriminative sequence motifs from learning machines","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Banting and Best Diabetes Centre, University of Toronto; Bundesministerium für Bildung und Forschung; National Research Foundation of Korea; Deutsche Forschungsgemeinschaft; National Research Foundation","keywords":"Artificial intelligence; Computer science; Support vector machine; Machine learning; Discriminative model; Pattern recognition (psychology); Computation; Data mining; Algorithm","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001429554,0.0001254191,0.0001743187,0.00002664779,0.0002284215,0.00005035094,0.0003157727,0.0001070312,0.00009829932],"category_scores_gemma":[0.0009092842,0.0001193529,0.0000483536,0.00001960864,0.0001197443,0.00001910259,0.0001731319,0.0002196671,0.00002028297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001170791,"about_ca_system_score_gemma":0.00002534333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003616473,"about_ca_topic_score_gemma":0.00003509242,"domain_scores_codex":[0.9992125,0.00003890233,0.0002073013,0.0001904816,0.000198664,0.0001521257],"domain_scores_gemma":[0.998928,0.00002312934,0.0003864341,0.0005038199,0.0001123688,0.00004626921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004294816,0.0002364654,0.0795703,0.00009285529,0.0001484273,0.000001323983,0.000275768,0.0003092338,0.9176398,0.00006259709,0.0001120613,0.001508248],"study_design_scores_gemma":[0.0005011153,0.0003532445,0.0519352,0.0002088312,0.0001315165,0.000002577761,0.0001561414,0.03123046,0.9138771,0.0003489821,0.0009741231,0.0002807395],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898012,0.00008531959,0.001249948,0.0001665653,0.00007354876,0.0001145684,0.00002842414,0.00001538635,0.008465071],"genre_scores_gemma":[0.9839199,0.0001252228,0.01329759,0.00002512732,0.0001439,0.000008379259,0.0002523411,0.00001775001,0.002209762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03092123,"threshold_uncertainty_score":0.4867069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04477034974656711,"score_gpt":0.2942483855799259,"score_spread":0.2494780358333588,"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."}}