{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002541378,0.0003304464,0.0004703583,0.0000438395,0.0003384397,0.00007220246,0.0007465156,0.0002356003,0.00006565735],"category_scores_gemma":[0.0002929016,0.0003456644,0.000225165,0.0001155443,0.0003101677,0.0001671448,0.000266834,0.0003167972,0.000003727919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001414309,"about_ca_system_score_gemma":0.0001047479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004871132,"about_ca_topic_score_gemma":0.00002286399,"domain_scores_codex":[0.9982849,0.00004024696,0.0005396335,0.0004892242,0.0003040969,0.0003419055],"domain_scores_gemma":[0.9976147,0.0000306115,0.001051101,0.001031604,0.0001815648,0.00009044461],"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":[0.0002373819,0.0001400051,0.00495931,0.0003322763,0.00007237339,0.00001446022,0.00004928553,0.0002944334,0.9864733,0.005587007,0.000002246048,0.001837917],"study_design_scores_gemma":[0.0008850361,0.00009618212,0.0001716087,0.0001617069,0.0000890614,0.000002815984,0.00004952973,0.001953443,0.9925759,0.003547317,0.0001424517,0.0003249673],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9686232,0.000876538,0.02270561,0.00006599945,0.00004070786,0.0007517287,0.0002033167,0.0001160241,0.006616923],"genre_scores_gemma":[0.9412642,0.0001713089,0.05784866,0.000003006449,0.0000306419,0.0003121285,0.0001769173,0.00005488448,0.0001382432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03514305,"threshold_uncertainty_score":0.9998995,"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."}}