{"id":"W4411589277","doi":"10.1016/j.jmccpl.2025.100451","title":"Real-Time TIMSrescore: timsTOF-optimized PSM Rescoring Boosts Identification Rates of Modified and Unmodified Peptide and Protein Identifications Using Collisional Cross Section, Retention Time, and Fragmentation Patterns, in Real-Time","year":2025,"lang":"en","type":"article","venue":"Journal of Molecular and Cellular Cardiology Plus","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Bruker (Canada)","funders":"","keywords":"Fragmentation (computing); Retention time; Identification (biology); Chemistry; Chromatography; Computer science; Biology; Operating system","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":[],"consensus_categories":[],"category_scores_codex":[0.0006248959,0.0001754198,0.0004426475,0.0002328251,0.0001767135,0.00007239889,0.00009435841,0.0002158196,0.000008265264],"category_scores_gemma":[0.00007106689,0.00018584,0.00008057,0.0001586096,0.0001794625,0.0001923165,0.00008651252,0.0002296683,4.008221e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001029128,"about_ca_system_score_gemma":0.0000743392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007560672,"about_ca_topic_score_gemma":0.000001197868,"domain_scores_codex":[0.9984418,0.0001325048,0.0007685074,0.0003447708,0.0001556684,0.0001567934],"domain_scores_gemma":[0.9988242,0.00007340127,0.0005680781,0.0002210496,0.000245198,0.00006802053],"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":[0.0002420821,0.00002075769,0.001452394,0.0001220065,0.00008468504,0.00001563607,0.00006563665,0.004046604,0.9935459,0.0002020674,0.00001579391,0.000186411],"study_design_scores_gemma":[0.002790244,0.00006981879,0.008962285,0.000565086,0.0002727914,0.0001845025,0.0001325397,0.04067169,0.9420044,0.00402137,0.00002169399,0.0003036167],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9519075,0.0005302429,0.04680154,0.00005688584,0.00002523885,0.0003961808,0.0000384437,0.0000173431,0.0002266631],"genre_scores_gemma":[0.9879897,0.001014251,0.01013285,0.000005691697,0.00003888817,0.00004789357,0.000105294,0.00002062715,0.0006448092],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05154156,"threshold_uncertainty_score":0.7578334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01439880299211767,"score_gpt":0.2884824570034818,"score_spread":0.2740836540113641,"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."}}