{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219031,0.001322835,0.0007332311,0.0008439618,0.0006348133,0.001439737,0.002181224,0.0009459827,0.008169916],"category_scores_gemma":[0.001560693,0.0006620769,0.0009075211,0.0006648556,0.0004873598,0.001151832,0.001394855,0.001682049,0.005674219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007782969,"about_ca_system_score_gemma":0.001580781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002560363,"about_ca_topic_score_gemma":0.006089346,"domain_scores_codex":[0.999371,0.00004739879,0.00004630305,0.0001513722,0.0003045374,0.00007943386],"domain_scores_gemma":[0.9993616,0.0001423829,0.00007411114,0.0001401447,0.000219693,0.0000621429],"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":[0.001817199,0.0002656558,0.009529469,0.0008002645,0.0003474035,0.0008854308,0.0004203313,0.01609232,0.4159405,0.005810713,0.1710685,0.3770222],"study_design_scores_gemma":[0.0001772019,0.0002882839,0.006711611,0.00005991471,0.00008995524,0.001027021,0.00012554,0.3212253,0.5523636,0.005449384,0.1121959,0.000286313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09443087,0.001578535,0.6704978,0.00121332,0.0003327148,0.0005407307,0.01548537,0.2029399,0.01298065],"genre_scores_gemma":[0.1456824,0.0006432073,0.7929334,0.001267428,0.0001228121,0.0007679149,0.03555959,0.01112532,0.01189802],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008169916,"threshold_uncertainty_score":0.02733111,"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."}}