{"id":"W4393118151","doi":"10.1101/2024.03.20.586018","title":"Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"Inflammatory Bowel and Immunobiology Research Institute; Cedars-Sinai Medical Center; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Biomarker discovery; Biomarker; Computational biology; Computer science; Bioinformatics; Medicine; Oncology; Data mining; Proteomics; Chemistry; 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":[0.02456878,0.002484283,0.001985313,0.004542022,0.001085275,0.007402225,0.002875014,0.001501694,0.004295244],"category_scores_gemma":[0.02467803,0.001480709,0.001783567,0.002818616,0.00135213,0.00402446,0.0071102,0.004700022,0.008216852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105112,"about_ca_system_score_gemma":0.006000805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007605811,"about_ca_topic_score_gemma":0.001113378,"domain_scores_codex":[0.9937832,0.0018518,0.0006795421,0.001654355,0.001743637,0.0002874781],"domain_scores_gemma":[0.9848894,0.005065165,0.00138474,0.002943764,0.004628951,0.001087955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001972666,0.0006155089,0.008223093,0.00196367,0.0005775481,0.001064564,0.002005475,0.005978324,0.2288163,0.0214183,0.05320428,0.6741602],"study_design_scores_gemma":[0.0008502134,0.002246111,0.00984725,0.0008842048,0.0005050834,0.002590107,0.0009111248,0.1410431,0.3786486,0.1616268,0.3000827,0.00076461],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01506545,0.001813135,0.946018,0.004972178,0.0004287563,0.00106225,0.003162823,0.02463465,0.002842661],"genre_scores_gemma":[0.04370143,0.001252071,0.9422276,0.00182529,0.0002204365,0.001106638,0.00561617,0.002333173,0.001717225],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02456878,"threshold_uncertainty_score":0.1299337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0344465996387007,"score_gpt":0.2999293772774576,"score_spread":0.2654827776387569,"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."}}