{"id":"W2593349649","doi":"10.1074/mcp.m116.066233","title":"Machine Learning of Global Phosphoproteomic Profiles Enables Discrimination of Direct versus Indirect Kinase Substrates","year":2017,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec; Institute for Research in Immunology and Cancer","funders":"National Center for Research Resources; Genome Canada; Canadian Institutes of Health Research; National Institutes of Health; Canada Research Chairs; Fonds de Recherche du Québec - Santé","keywords":"Phosphoproteomics; Autophosphorylation; Phosphorylation; Dephosphorylation; Kinase; Cell biology; Biochemistry; Phosphatase; Substrate-level phosphorylation; Protein phosphorylation; Cyclin-dependent kinase 1; Biology; Chemistry; Protein kinase A; Cell cycle","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007730419,0.0009621754,0.0009576985,0.001421124,0.0002389692,0.0008588077,0.0003838752,0.0004090296,0.0008020477],"category_scores_gemma":[0.001011812,0.0002911312,0.0007002926,0.001179166,0.0003818279,0.0008568656,0.0005855134,0.0008863942,0.000457176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003330672,"about_ca_system_score_gemma":0.0003067524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000456088,"about_ca_topic_score_gemma":0.0007006417,"domain_scores_codex":[0.9996998,0.0000436375,0.0000181507,0.0001582,0.00004857625,0.00003157007],"domain_scores_gemma":[0.9995753,0.000153727,0.0001151665,0.00007763421,0.00004655599,0.00003159589],"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":[0.0006264678,0.000280564,0.02423743,0.0005595034,0.0004467696,0.0001927085,0.0001078313,0.04129501,0.6611518,0.002915968,0.00148771,0.2666982],"study_design_scores_gemma":[0.0000436359,0.0005867017,0.08192705,0.00006190366,0.0002631356,0.0004270958,0.0001150141,0.6384283,0.2466394,0.02426432,0.007133925,0.0001095186],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4023538,0.001957134,0.5879058,0.0003236727,0.0000431089,0.00009253281,0.003315192,0.002383215,0.001625616],"genre_scores_gemma":[0.8052758,0.001872996,0.186975,0.00009500128,0.00004624521,0.0001577122,0.004388998,0.0001887619,0.0009994572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001421124,"threshold_uncertainty_score":0.004088283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374767708755373,"score_gpt":0.2619846309687603,"score_spread":0.2482369538812066,"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."}}