{"id":"W4403601159","doi":"10.1002/anie.202409446","title":"A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data‐Independent Acquisition Mass Spectrometry (DIA‐MS) Discovery","year":2024,"lang":"en","type":"article","venue":"Angewandte Chemie International Edition","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; Inflammatory Bowel and Immunobiology Research Institute; Cedars-Sinai Medical Center; National Institutes of Health; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Biomarker discovery; Mass spectrometry; Proteomics; Biomarker; Pipeline (software); Label-free quantification; Selected reaction monitoring; Tandem mass spectrometry; Computational biology; Proteome; Computer science; Data mining; Bioinformatics; Chemistry; Quantitative proteomics; Chromatography; Biology","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":[0.009167266,0.001683774,0.001195322,0.003295467,0.0007778654,0.002802147,0.001219233,0.0008380772,0.0033634],"category_scores_gemma":[0.007330571,0.001130889,0.001081293,0.002212723,0.0005488848,0.001923488,0.002549913,0.001974345,0.006096166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008727546,"about_ca_system_score_gemma":0.003462273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009821698,"about_ca_topic_score_gemma":0.001480911,"domain_scores_codex":[0.9972791,0.0004511122,0.0003123065,0.000675102,0.001102974,0.0001793711],"domain_scores_gemma":[0.9962412,0.001053631,0.0004702251,0.0005415126,0.001416411,0.0002769509],"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":[0.001225634,0.0003817909,0.01016133,0.001134115,0.0003799365,0.000618603,0.000469387,0.00523196,0.5944066,0.006937424,0.03440319,0.3446501],"study_design_scores_gemma":[0.000501068,0.001528074,0.01981777,0.0003170245,0.0003119328,0.002545151,0.0002209579,0.1418656,0.6527296,0.02627245,0.1534614,0.0004288413],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01912085,0.001631837,0.9422783,0.0009686124,0.0001438807,0.001518182,0.006822999,0.02449773,0.003017633],"genre_scores_gemma":[0.07599703,0.001413725,0.9033311,0.0008566013,0.00008025072,0.001628144,0.0122777,0.001448198,0.002967312],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009167266,"threshold_uncertainty_score":0.0484817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04120628333250079,"score_gpt":0.3536298726550236,"score_spread":0.3124235893225228,"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."}}