{"id":"W2111693807","doi":"10.1093/bioinformatics/btq266","title":"DETECT—a Density Estimation Tool for Enzyme ClassificaTion and its application to <i>Plasmodium falciparum</i>","year":2010,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Canada Research Chairs; Hospital for Sick Children; University of Toronto","funders":"Ontario Ministry of Research and Innovation; Canadian Institutes of Health Research","keywords":"Annotation; Ranking (information retrieval); Computer science; Computational biology; Probabilistic logic; Plasmodium falciparum; Sequence alignment; Genomics; Data mining; Biology; Machine learning; Artificial intelligence; Genetics; Gene; Peptide sequence; Genome; Malaria","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":[0.0003960239,0.0001782552,0.0001358307,0.00007376644,0.0001754456,0.00007203775,0.0001915618,0.0002341136,0.000002996341],"category_scores_gemma":[0.0005878659,0.0001762408,0.00004795111,0.00009999645,0.00003375824,0.00002813641,0.0001043807,0.000142932,0.00006213477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001403472,"about_ca_system_score_gemma":0.00005103119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002266981,"about_ca_topic_score_gemma":0.00002285997,"domain_scores_codex":[0.9990059,0.00001221197,0.0004183156,0.00017403,0.0001524772,0.0002370857],"domain_scores_gemma":[0.9990544,0.00003608609,0.0002155392,0.0003922908,0.0001859502,0.0001157343],"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":[0.0001740964,0.0000536362,0.001603196,0.0004963477,0.00004348255,9.11305e-8,0.0006363132,0.001573716,0.8977702,0.005149264,0.005473072,0.08702658],"study_design_scores_gemma":[0.0005214654,0.0002299384,0.004603269,0.000009821512,0.00002948146,0.00002548135,0.00005108332,0.7769703,0.1665446,0.0001380632,0.05052901,0.0003474379],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5454472,0.00001185192,0.4527078,0.0002494373,0.00015757,0.0009009774,0.00003795414,0.00004650729,0.0004406407],"genre_scores_gemma":[0.8224545,0.00001209703,0.176212,0.0005539017,0.0001126943,0.0001473477,0.0003689548,0.00001921603,0.0001193384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7753966,"threshold_uncertainty_score":0.7186892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009605648486673174,"score_gpt":0.2615764797752238,"score_spread":0.2519708312885506,"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."}}