{"id":"W2140572608","doi":"10.1186/1471-2105-11-s1-s4","title":"Better score function for peptide identification with ETD MS/MS spectra","year":2010,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Bioinformatics Solutions (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Electron-transfer dissociation; Tandem mass spectrometry; Proteomics; Peptide; Mass spectrometry; Database search engine; Computer science; Computational biology; Tandem mass tag; Bioinformatics; Chemistry; Chromatography; Quantitative proteomics; Search engine; Biology; Information retrieval; Biochemistry","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":[0.0001021365,0.0001415373,0.0001094979,0.00004127904,0.0001647868,0.00007947761,0.0001823392,0.0001335483,0.0001407779],"category_scores_gemma":[0.00002581012,0.0001211209,0.00006013692,0.00009427451,0.00006282588,0.0002900775,0.00002545971,0.0002345089,0.00005264971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000286097,"about_ca_system_score_gemma":0.00004017951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004141163,"about_ca_topic_score_gemma":0.00005536549,"domain_scores_codex":[0.9991905,0.000001332953,0.0003446963,0.0001391725,0.0001305742,0.0001937272],"domain_scores_gemma":[0.9990347,0.0000401458,0.0002550683,0.000506344,0.0001050919,0.00005864034],"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":[0.0004761094,0.0003893487,0.01685498,0.003101759,0.0001343317,7.073864e-7,0.001118721,0.0005486328,0.7960458,0.1077098,0.01887181,0.05474805],"study_design_scores_gemma":[0.001334608,0.0001225633,0.002349605,0.0001113285,0.0001485452,0.0000443405,0.0003180641,0.1035951,0.7310668,0.02650104,0.1335474,0.0008605824],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08586935,0.000003575655,0.9042176,0.0001253593,0.00004821312,0.0004169717,0.00006684296,0.0002278413,0.00902423],"genre_scores_gemma":[0.0622045,0.000007001179,0.93523,0.0001542543,0.0002537995,0.0005130203,0.000363163,0.00003353634,0.001240746],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1146756,"threshold_uncertainty_score":0.4939167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536811295687333,"score_gpt":0.2511999830844915,"score_spread":0.2358318701276182,"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."}}