{"id":"W2914043673","doi":"10.1016/j.compbiolchem.2019.01.014","title":"Identification of coenzyme-binding proteins with machine learning algorithms","year":2019,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"National Natural Science Foundation of China","keywords":"Coenzyme A; Cofactor; Random forest; Biochemistry; Classifier (UML); Binding site; Machine learning; Protein sequencing; Enzyme; Computational biology; Biology; Gene; Artificial intelligence; Algorithm; Peptide sequence; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002670089,0.0000994334,0.0001404608,0.00003944994,0.00007265261,0.00002869736,0.000225411,0.0000601662,0.00001929385],"category_scores_gemma":[0.00003705553,0.00008881689,0.00002520634,0.0001774671,0.00009059118,0.000136032,0.0001173648,0.0001391053,0.000009475034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001725542,"about_ca_system_score_gemma":0.00007712338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000352432,"about_ca_topic_score_gemma":1.492892e-7,"domain_scores_codex":[0.9991753,0.00005340541,0.0002191743,0.0003139457,0.0001268649,0.0001113533],"domain_scores_gemma":[0.9992499,0.0002818512,0.0001872982,0.0001255992,0.0001185595,0.0000367594],"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":[0.0001194944,0.0002603288,0.08078039,0.0004792096,0.0002420511,0.000008848699,0.0007528106,0.3571088,0.4248781,0.1008542,0.00004932892,0.03446647],"study_design_scores_gemma":[0.0005999277,0.0000902744,0.009348586,0.00003323129,0.000007108887,0.00006789699,0.00002944623,0.8993812,0.07106079,0.01900777,0.0001748447,0.0001989712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.496995,0.0001051513,0.5024571,0.0001305122,0.0000340453,0.00007829754,0.000008315542,0.00003058822,0.0001610223],"genre_scores_gemma":[0.9496188,0.000003988175,0.05008161,0.00002285694,0.00002193683,0.000008926115,0.00009570627,0.000004881804,0.0001412465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5422723,"threshold_uncertainty_score":0.3621848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00934195659620469,"score_gpt":0.2722948902982095,"score_spread":0.2629529337020048,"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."}}