{"id":"W2368906419","doi":"10.1186/s13040-016-0098-0","title":"Machine learning algorithms for mode-of-action classification in toxicity assessment","year":2016,"lang":"en","type":"article","venue":"BioData Mining","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"MacEwan University; University of Calgary; Alberta Health; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Action (physics); Mode (computer interface); Machine learning; Artificial intelligence; Mode of action; Algorithm; Chemistry; Human–computer interaction","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":[0.0001939269,0.00007786582,0.00008630115,0.00006224531,0.00004311485,0.000008671505,0.0001100492,0.0000879584,0.00001284682],"category_scores_gemma":[0.00008603922,0.00006096068,0.00003399295,0.00007134599,0.00002515024,0.000009848298,0.00004102308,0.00003311305,0.000001022783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003188599,"about_ca_system_score_gemma":0.00005573585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000997814,"about_ca_topic_score_gemma":0.00001847695,"domain_scores_codex":[0.9992779,0.00004659194,0.0002015511,0.000274943,0.00008135638,0.000117594],"domain_scores_gemma":[0.9995379,0.0000190751,0.0001415713,0.0002205132,0.00005059589,0.00003030297],"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":[0.00004520913,0.00003094857,0.009402194,0.00001027424,0.0000059028,4.998762e-8,0.0000180644,0.00001061183,0.8775288,0.00008457217,0.0002919388,0.1125714],"study_design_scores_gemma":[0.001068981,0.0002304136,0.02401192,0.00005769706,0.000008593912,0.000001424417,0.0002654099,0.01055315,0.8944213,0.00003987935,0.06917287,0.0001683956],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7219605,0.0001736889,0.276395,0.0004426615,0.0002135005,0.0002469754,0.0001038786,0.00001531479,0.0004485161],"genre_scores_gemma":[0.9904549,0.0002305458,0.008397649,0.00002443835,0.0000861273,0.00007102003,0.0003313754,0.00001082046,0.0003931295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2684944,"threshold_uncertainty_score":0.2485904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08482953243657332,"score_gpt":0.3714903720574589,"score_spread":0.2866608396208856,"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."}}