{"id":"W4405130370","doi":"10.1021/acs.jproteome.4c00689","title":"ProPickML: Advancing Clinical Diagnostics with Automated Peak Picking in Label-Free Targeted Proteomics","year":2024,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier universitaire de Québec","funders":"Fonds de recherche du Québec – Nature et technologies; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Proteomics; Computational biology; Chemistry; Computer science; Nanotechnology; Biology; Materials science; Biochemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.009985959,0.002773679,0.001475313,0.003090024,0.000683082,0.003698181,0.004161289,0.002313835,0.007928371],"category_scores_gemma":[0.02757193,0.001275449,0.001436545,0.001732951,0.001028626,0.002845762,0.004134323,0.002803677,0.006178363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077552,"about_ca_system_score_gemma":0.003139605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002011522,"about_ca_topic_score_gemma":0.002892622,"domain_scores_codex":[0.9956398,0.00144234,0.0002609087,0.0008269669,0.001660631,0.0001694057],"domain_scores_gemma":[0.9882544,0.00796326,0.001330351,0.001082884,0.001116825,0.0002523526],"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":[0.002515188,0.000491946,0.01277511,0.002994309,0.0007986628,0.001207983,0.000574287,0.06468951,0.07361251,0.01420584,0.08993033,0.7362043],"study_design_scores_gemma":[0.0003157073,0.0004223412,0.003072781,0.0002849856,0.0001375719,0.001641191,0.00007170142,0.8263605,0.09822104,0.02167309,0.04750943,0.0002897608],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006405731,0.000745768,0.8930815,0.0006121885,0.000122133,0.0002058985,0.002304221,0.09528962,0.001233005],"genre_scores_gemma":[0.05292875,0.0005552735,0.9357579,0.0007409363,0.0000941018,0.0004876056,0.003324113,0.004804733,0.001306625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009985959,"threshold_uncertainty_score":0.05281138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0657273592473595,"score_gpt":0.4437996282340583,"score_spread":0.3780722689866988,"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."}}