{"id":"W2274817843","doi":"10.1016/j.dib.2016.02.036","title":"Active site specificity profiling datasets of matrix metalloproteinases (MMPs) 1, 2, 3, 7, 8, 9, 12, 13 and 14","year":2016,"lang":"en","type":"article","venue":"Data in Brief","topic":"Peptidase Inhibition and Analysis","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation; Deutsche Forschungsgemeinschaft; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Deutscher Akademischer Austauschdienst; Michael Smith Health Research BC; University of British Columbia; Alexander von Humboldt-Stiftung","keywords":"Cleavage (geology); Protease; Computational biology; Peptide; Matrix metalloproteinase; Chemistry; Prime (order theory); Biology; Biochemistry; Enzyme; Mathematics","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":[0.0003135037,0.0001172349,0.000322735,0.0001509143,0.00002823585,0.00001283634,0.0001443621,0.00005403423,0.0004153733],"category_scores_gemma":[0.0004659214,0.00008122694,0.0000346174,0.0001754818,0.0001349998,0.0003753814,0.000261322,0.0000943994,0.00003174537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003203874,"about_ca_system_score_gemma":0.00004683766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004233297,"about_ca_topic_score_gemma":0.0003059216,"domain_scores_codex":[0.9988678,0.00005096689,0.0002955189,0.0003926284,0.0002304483,0.0001627068],"domain_scores_gemma":[0.9988018,0.00009964131,0.0001159317,0.0008460528,0.00004314764,0.00009343],"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":[0.001243799,0.0008697874,0.03911888,0.0005740585,0.0003764681,0.0005072273,0.0001726629,0.000001910162,0.8965391,0.0009537929,0.02700416,0.03263815],"study_design_scores_gemma":[0.009626807,0.0003458288,0.08969184,0.003459353,0.000551005,0.0001690773,0.0004901852,0.001032293,0.8169985,0.00042253,0.07647606,0.0007365373],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9721428,0.0001395039,0.0006781392,0.0007625517,0.00001401551,0.0003218975,0.02534461,0.00002816922,0.0005683021],"genre_scores_gemma":[0.9878308,0.0001321893,0.002151303,0.000163822,0.00008129,0.000008491984,0.009324196,0.0000126425,0.0002952235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07954061,"threshold_uncertainty_score":0.4548047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0373958250872307,"score_gpt":0.3133078067048847,"score_spread":0.275911981617654,"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."}}