{"id":"W4413159407","doi":"10.1016/j.mcpro.2025.101050","title":"A Novel Hybrid High-Speed Mass Spectrometer Allows Rapid Translation From Biomarker Candidates to Targeted Clinical Tests Using 15N-Labeled Proteins","year":2025,"lang":"en","type":"article","venue":"Molecular & Cellular Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thermo Fisher Scientific (Canada)","funders":"Leibniz-Gemeinschaft; Bayerisches Staatsministerium für Wissenschaft und Kunst; Horizon 2020 Framework Programme; International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; International Max Planck Research School for Advanced Methods in Process and Systems Engineering; Max-Planck-Gesellschaft; European Commission","keywords":"Mass spectrometry; Translation (biology); Biomarker; Computational biology; Proteomics; Computer science; Chemistry; Biology; Chromatography; Genetics; Gene; Messenger RNA","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":[0.001825468,0.0007531584,0.0005595344,0.0009993855,0.0003241326,0.0008978097,0.0007307356,0.001263691,0.001574947],"category_scores_gemma":[0.001202115,0.000382858,0.0004269595,0.0004620791,0.0005080756,0.001000732,0.00088614,0.0005689649,0.0008855222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000335976,"about_ca_system_score_gemma":0.0004960352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002190668,"about_ca_topic_score_gemma":0.0004406317,"domain_scores_codex":[0.9988348,0.0001865773,0.00006159725,0.000346424,0.0005224682,0.00004820132],"domain_scores_gemma":[0.9991934,0.0002693251,0.0001805318,0.00008287759,0.0001878871,0.0000860397],"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":[0.0003464106,0.00006922498,0.001938502,0.0001417345,0.00008229228,0.00006069426,0.0000268554,0.0006568798,0.9605597,0.001046906,0.001024732,0.03404607],"study_design_scores_gemma":[0.0002126258,0.002387028,0.01011955,0.00004342805,0.000178439,0.003294348,0.0000779401,0.06286988,0.8916062,0.001808173,0.02724307,0.0001592898],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4413374,0.004643211,0.5363246,0.0008954756,0.0005868776,0.0005734877,0.001730218,0.006090889,0.007817844],"genre_scores_gemma":[0.3895907,0.001329384,0.5993907,0.001199334,0.0002744709,0.000413775,0.001638558,0.0002249892,0.005938007],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001825468,"threshold_uncertainty_score":0.009654105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02427828363488532,"score_gpt":0.2974750790488634,"score_spread":0.2731967954139781,"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."}}