{"id":"W2326414500","doi":"10.1021/pr200153k","title":"PeaksPTM: Mass Spectrometry-Based Identification of Peptides with Unspecified Modifications","year":2011,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":179,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Identification (biology); Tandem mass spectrometry; Database search engine; Mass spectrometry; Sequence (biology); Software; Posttranslational modification; Data mining; Computational biology; Combinatorial chemistry; Chemistry; Information retrieval; Search engine; Chromatography; Programming language; Biochemistry; Biology","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":[0.001362096,0.0001208822,0.0002413533,0.0004961758,0.0001542499,0.00003493626,0.0006565804,0.0000968027,0.0004462961],"category_scores_gemma":[0.0001815364,0.0000976037,0.0001047857,0.0007925189,0.0002971396,0.0001898389,0.00003564096,0.0007057769,0.00001593113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001661528,"about_ca_system_score_gemma":0.0002739991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002084764,"about_ca_topic_score_gemma":0.000001519323,"domain_scores_codex":[0.9979225,0.00006015759,0.0007334185,0.0002084363,0.0007862519,0.0002891893],"domain_scores_gemma":[0.9969305,0.00009695039,0.0007241627,0.0005933005,0.001522739,0.0001323768],"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":[0.0002235079,0.000257537,0.0005565058,0.0001206033,0.00003393669,0.000007385509,0.0001254565,0.00003772773,0.9925321,0.005736838,0.00009643671,0.0002719055],"study_design_scores_gemma":[0.0003752669,0.0002079811,0.0007421356,0.0001146452,0.0000162161,0.00002632703,0.0002864758,0.000208795,0.9789759,0.01865823,0.0002836028,0.0001044268],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3289519,0.0001474285,0.653273,0.000648926,0.00001424657,0.0006928726,0.00004242127,0.00005022882,0.01617889],"genre_scores_gemma":[0.7259152,0.00005919055,0.2732289,0.000002022426,0.00006779585,0.0001241396,0.000003711164,0.00002353682,0.0005755054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3969633,"threshold_uncertainty_score":0.4886629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1126874813581207,"score_gpt":0.3659287734738018,"score_spread":0.253241292115681,"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."}}