{"id":"W2979759276","doi":"10.1109/tcbb.2019.2945954","title":"ChimST: An Efficient Spectral Library Search Tool for Peptide Identification from Chimeric Spectra in Data-Dependent Acquisition","year":2019,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Identification (biology); Set (abstract data type); Peptide; Shotgun proteomics; Database search engine; Search engine; Shotgun; Computational biology; Proteomics; Information retrieval; Chemistry; Biology; Programming language","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":[0.0001445792,0.0001660468,0.0001763239,0.000135559,0.0001716382,0.00006398789,0.0003681425,0.0001511454,0.000213784],"category_scores_gemma":[0.000004979042,0.0001631704,0.0000453936,0.0001407023,0.00007571709,0.0003979926,0.00001788489,0.0002484858,0.00004102732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004627544,"about_ca_system_score_gemma":0.00006246071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001379054,"about_ca_topic_score_gemma":0.000005802781,"domain_scores_codex":[0.9988297,0.00002017582,0.0004712843,0.0003534036,0.0001176539,0.000207812],"domain_scores_gemma":[0.9990082,0.0002515769,0.0001274472,0.0005200401,0.00003622225,0.00005651752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001280315,0.00224001,0.005508311,0.000574449,0.0002964621,0.000002812081,0.002008624,0.7800338,0.100656,0.01372933,0.0001860517,0.09348387],"study_design_scores_gemma":[0.001158701,0.0001438233,0.003123546,0.00005212785,0.00003281763,0.00001499784,0.0003021947,0.9209433,0.05035171,0.02326935,0.0002263599,0.000381062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.458355,0.00001404343,0.5390439,0.0002687796,0.00005690256,0.0003644401,0.001716518,0.00008497595,0.0000953706],"genre_scores_gemma":[0.7311864,0.00005486617,0.2639991,0.0001438622,0.00006643478,0.000079276,0.004377636,0.00001520411,0.00007721818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2750449,"threshold_uncertainty_score":0.6653896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01914777984649685,"score_gpt":0.2964362172501698,"score_spread":0.2772884374036729,"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."}}