{"id":"W2956732318","doi":"10.1021/acs.jproteome.9b00205","title":"PTMProphet: Fast and Accurate Mass Modification Localization for the Trans-Proteomic Pipeline","year":2019,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":160,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Allergy and Infectious Diseases; National Institute on Aging; National Heart, Lung, and Blood Institute; Medical Research Council; National Institute of General Medical Sciences; Cancer Research UK","keywords":"Pipeline (software); Computer science; False discovery rate; Set (abstract data type); Proteome; Sequence database; Proteomics; Database search engine; Ground truth; Sequence (biology); Computational biology; Data mining; Data set; Function (biology); False positive paradox; Algorithm; Bioinformatics; Chemistry; Biology; Search engine; Artificial intelligence; Information retrieval; Biochemistry; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003454139,0.003019528,0.00160272,0.002610493,0.0009554015,0.002858565,0.002557994,0.001059736,0.01834575],"category_scores_gemma":[0.006616364,0.001486201,0.00201606,0.001535025,0.000618553,0.002865335,0.00332048,0.002817217,0.0129163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105727,"about_ca_system_score_gemma":0.002089172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002003654,"about_ca_topic_score_gemma":0.002926739,"domain_scores_codex":[0.9986784,0.0001476496,0.0001285571,0.0004337514,0.0004828452,0.0001287959],"domain_scores_gemma":[0.9986175,0.000520431,0.0001866277,0.0002757213,0.0002906157,0.0001091006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004990351,0.0004142482,0.01367463,0.003356454,0.001589883,0.001627387,0.001074797,0.02310346,0.1520005,0.01342256,0.4501954,0.3345502],"study_design_scores_gemma":[0.0008401509,0.0005438788,0.01289344,0.0002761571,0.0002779042,0.002204248,0.0002429927,0.5767567,0.2216789,0.02794368,0.1556556,0.000686479],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01599627,0.0003541205,0.5161264,0.00026799,0.000194968,0.0002623414,0.02653338,0.4378441,0.002420579],"genre_scores_gemma":[0.1074596,0.0006279009,0.7292578,0.0007552656,0.0001244245,0.001924556,0.08812454,0.06494169,0.006784214],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01834575,"threshold_uncertainty_score":0.06137264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06656372185227585,"score_gpt":0.3885289544544264,"score_spread":0.3219652326021505,"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."}}