{"id":"W4407259436","doi":"10.1021/acs.jproteome.4c00609","title":"TIMS<sup>2</sup>Rescore: A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS<sup>2</sup>Rescore","year":2025,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bruker (Canada)","funders":"Universiteit Gent; Centre National de la Recherche Scientifique; Vlaamse regering; Deutsche Krebshilfe; Institut National de la Santé et de la Recherche Médicale; Université de Strasbourg; Deutschen Konsortium für Translationale Krebsforschung; Fonds Wetenschappelijk Onderzoek; Bundesministerium für Bildung und Forschung; CHIST-ERA; HORIZON EUROPE Framework Programme; Heidelberger Zentrum für Personalisierte Onkologie Deutsches Krebsforschungszentrum In Der Helmholtz-Gemeinschaft; Agence Nationale de la Recherche; Deutsche Forschungsgemeinschaft; Deutsches Krebsforschungszentrum","keywords":"Computer science; Proteomics; Workflow; Pipeline (software); Metaproteomics; Identification (biology); Fragmentation (computing); Computational biology; Data mining; Bioinformatics; Data science; Chemistry; Biology; Database","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002994253,0.0003492688,0.0005813638,0.0006709627,0.0006319951,0.0004071296,0.002257719,0.0003088716,0.0004794822],"category_scores_gemma":[0.001022245,0.0003445304,0.0001055048,0.0006935502,0.0002055057,0.001117826,0.001434457,0.001715093,0.00001858757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004404136,"about_ca_system_score_gemma":0.0007109679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001142762,"about_ca_topic_score_gemma":0.000009384531,"domain_scores_codex":[0.9953083,0.0002941552,0.001254274,0.000957578,0.001522373,0.000663361],"domain_scores_gemma":[0.9952428,0.0007526395,0.0005394411,0.002343048,0.0008238539,0.0002982103],"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":[0.008183109,0.001134259,0.001832929,0.001011224,0.0003182592,0.000155057,0.0003446063,0.8789625,0.08096704,0.0009485045,0.01072588,0.01541664],"study_design_scores_gemma":[0.005140373,0.0002140091,0.0001482038,0.001091982,0.00009164014,0.00003739127,0.000306683,0.9690316,0.01724051,0.003688619,0.002632353,0.0003765872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4301982,0.0005265629,0.5552194,0.006663174,0.00008880372,0.003747753,0.001559729,0.0002050058,0.001791383],"genre_scores_gemma":[0.5917683,0.0007960296,0.4021332,0.0002029013,0.001372939,0.0004782168,0.002118483,0.0001161835,0.001013687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1615701,"threshold_uncertainty_score":0.9999007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1126086908013007,"score_gpt":0.4224115342587788,"score_spread":0.3098028434574781,"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."}}