{"id":"W4403601158","doi":"10.1002/ange.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","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institutes of Health; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Mass spectrometry; Proteomics; Biomarker discovery; Pipeline (software); Chemistry; Biomarker; Chromatography; Computer science; Biochemistry","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":[0.007095259,0.001424341,0.0009784503,0.002862862,0.0006311447,0.002635265,0.001074234,0.000730345,0.003416552],"category_scores_gemma":[0.006128544,0.0009599033,0.0008883529,0.001736528,0.000427079,0.001582418,0.002316753,0.001437,0.005452615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008045948,"about_ca_system_score_gemma":0.002810884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008729351,"about_ca_topic_score_gemma":0.001193415,"domain_scores_codex":[0.9979309,0.0003547504,0.0002361972,0.0005396511,0.0007922167,0.0001463624],"domain_scores_gemma":[0.997184,0.0007739281,0.0003925005,0.0003790729,0.0010377,0.0002327836],"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":[0.001520402,0.0004173073,0.0112335,0.0009405987,0.0003655529,0.0008289774,0.0004072791,0.007273123,0.6174181,0.007739364,0.03378616,0.3180697],"study_design_scores_gemma":[0.0005163384,0.001244543,0.01729677,0.000256929,0.0002620274,0.001890915,0.0002259479,0.2085924,0.6207314,0.02684941,0.1217944,0.0003388514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02960532,0.001493175,0.9252097,0.001178806,0.0001461165,0.001460708,0.007181748,0.03035378,0.003370633],"genre_scores_gemma":[0.1352551,0.001125154,0.8447776,0.0008483697,0.00008601897,0.001329902,0.01183696,0.001657745,0.003083115],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007095259,"threshold_uncertainty_score":0.03752381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04813354872522741,"score_gpt":0.346973415381283,"score_spread":0.2988398666560556,"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."}}