{"id":"W2801106948","doi":"10.1093/bioinformatics/bty368","title":"Improved enzyme annotation with EC-specific cutoffs using DETECT v2","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Institute of Allergy and Infectious Diseases; Government of Ontario","keywords":"Annotation; Computer science; Enzyme; Artificial intelligence; Chemistry; Biochemistry","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":[0.0001939784,0.0002254186,0.0001634588,0.00006669768,0.0001966647,0.00009960379,0.0002145916,0.0001932604,0.00002376125],"category_scores_gemma":[0.00001294024,0.0001840801,0.00006465501,0.0001610456,0.0001765998,0.00002404749,0.0001003634,0.0001039505,0.00004927719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000300933,"about_ca_system_score_gemma":0.0001021614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009058133,"about_ca_topic_score_gemma":0.00003077834,"domain_scores_codex":[0.998877,0.00001440309,0.0004291271,0.0001585634,0.000148084,0.0003728088],"domain_scores_gemma":[0.9989883,0.000007803872,0.0002469072,0.0004580228,0.0001886899,0.0001103263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001707426,0.0002588108,0.00108752,0.0005521045,0.0007883311,0.000009104383,0.005631382,0.002387776,0.632609,0.001371474,0.04842819,0.3051689],"study_design_scores_gemma":[0.002381502,0.002539301,0.0003185836,0.00009351948,0.00007501874,0.0001967972,0.0008974392,0.5850778,0.1892418,0.0001905373,0.2178068,0.001180848],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4088677,0.0004042791,0.5828496,0.00004982371,0.0005196584,0.000578047,0.00004864403,0.00006285873,0.006619383],"genre_scores_gemma":[0.853604,0.0001195773,0.1443149,0.0005937697,0.0008555389,0.00001105295,0.0001899761,0.00004278983,0.0002683131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.58269,"threshold_uncertainty_score":0.7506569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264989105008768,"score_gpt":0.2272251615810608,"score_spread":0.2145752705309731,"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."}}