{"id":"W3094180207","doi":"10.1016/j.xpro.2020.100135","title":"Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome","year":2020,"lang":"en","type":"article","venue":"STAR Protocols","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Methylation; Proteome; Computational biology; Identification (biology); Protocol (science); Lysine; Human proteome project; Computer science; Variety (cybernetics); Mass spectrometry; Chemistry; Biology; Proteomics; Bioinformatics; Artificial intelligence; Biochemistry; Amino acid; Medicine; Ecology; Chromatography","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":[0.0002934598,0.0001499703,0.0001409499,0.00003237572,0.0001497242,0.00005643168,0.0001398016,0.00008093942,0.00003637503],"category_scores_gemma":[0.0002413318,0.0001138337,0.00004758671,0.0002008743,0.00003564161,0.000003913128,0.0001427352,0.0001638529,0.000006204648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001089302,"about_ca_system_score_gemma":0.0000388907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001515616,"about_ca_topic_score_gemma":0.000004758621,"domain_scores_codex":[0.9989511,0.000153706,0.0001842453,0.0003381642,0.0001537137,0.0002190666],"domain_scores_gemma":[0.9995401,0.00001182882,0.00007952242,0.0001747202,0.00005868662,0.0001351545],"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":[0.00009400463,0.00000878774,0.00341259,0.00003284381,0.0000235154,0.000001639244,0.0002104151,0.0001662849,0.9954073,0.00001275128,0.00001809942,0.0006117218],"study_design_scores_gemma":[0.000391257,0.0007729811,0.0006465161,0.00001474644,0.000009960071,9.479221e-7,0.0001219555,0.00131725,0.9464208,0.0002270253,0.04989711,0.0001794453],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8926373,0.0007670373,0.0538898,0.001478893,0.00002889395,0.05078852,0.00002196213,0.00003919574,0.0003484369],"genre_scores_gemma":[0.8956125,0.00004226383,0.08195885,0.0005693522,0.0005318674,0.02097918,0.0000621839,0.00007386225,0.0001698847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04987901,"threshold_uncertainty_score":0.4642004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07448302824115649,"score_gpt":0.3486864304206416,"score_spread":0.2742034021794851,"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."}}