{"id":"W4241891316","doi":"10.3410/f.735729556.793560891","title":"Faculty Opinions recommendation of A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.","year":2019,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Defense Threat Reduction Agency; Hansjörg Wyss Institute for Biologically Inspired Engineering, Harvard University; Novo Nordisk Fonden; Novo Nordisk; Paul G. Allen Frontiers Group; Broad Institute; McMaster University; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Machine learning; Artificial intelligence; White box; Computer science; White (mutation); Computational biology; Antibiotics; Deep learning; Action (physics); Biology; Genetics; Physics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001274549,0.001610527,0.0008706237,0.002437203,0.000531852,0.00183339,0.00233618,0.002124778,0.09098391],"category_scores_gemma":[0.006541769,0.0004800317,0.001047818,0.003182132,0.0003190788,0.001118536,0.001442061,0.001711041,0.09751014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0010142,"about_ca_system_score_gemma":0.001783904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009664547,"about_ca_topic_score_gemma":0.03711491,"domain_scores_codex":[0.9991421,0.0001322802,0.00008468438,0.0002418072,0.0002904749,0.0001086025],"domain_scores_gemma":[0.9968562,0.0008382242,0.0002991674,0.0008150022,0.0007153058,0.0004760174],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006810543,0.00005161997,0.001662886,0.0004287845,0.00003607186,0.00002601878,0.000007744106,0.0003869855,0.0002373249,0.0004233043,0.9918728,0.00479827],"study_design_scores_gemma":[0.0002673701,0.000041469,0.004975782,0.0001829049,0.00003697496,0.00006494168,0.00002700393,0.002187251,0.001096202,0.001465561,0.9896299,0.00002462697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005812922,0.0001158665,0.0003471814,0.0003366286,0.00009502968,0.00002959565,0.9945189,0.0009828194,0.002992704],"genre_scores_gemma":[0.001150936,0.00008084352,0.0008520878,0.0001941019,0.00001738312,0.00005000844,0.9954838,0.00009192119,0.002079029],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9987255,"threshold_uncertainty_score":0.3043715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06557518804656653,"score_gpt":0.385039539950126,"score_spread":0.3194643519035595,"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."}}