{"id":"W4389180765","doi":"10.1021/acs.analchem.3c03357","title":"Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion","year":2023,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Lunenfeld-Tanenbaum Research Institute","funders":"National Cancer Institute; National Institutes of Health; Outstanding Youth Scientist Foundation of Hunan Province; American Society for Mass Spectrometry; Science and Technology Bureau, Changsha; Mitacs; National Natural Science Foundation of China","keywords":"Reproducibility; Chemistry; Proteomics; Proteome; Quantitative proteomics; Chromatography; Mass spectrometry; Label-free quantification; Sample preparation; TRACE (psycholinguistics); 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":[0.002963461,0.001558747,0.0009337238,0.001426398,0.0007265647,0.001727307,0.001758541,0.0009973813,0.002345305],"category_scores_gemma":[0.003104532,0.0006945375,0.0006675546,0.001033802,0.0009995502,0.001575506,0.001636188,0.002107266,0.001428865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007631207,"about_ca_system_score_gemma":0.001223386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008814755,"about_ca_topic_score_gemma":0.001463008,"domain_scores_codex":[0.9973782,0.0003602337,0.000196945,0.0008785813,0.001002511,0.0001835113],"domain_scores_gemma":[0.9977047,0.0007950716,0.0002514235,0.0004080913,0.0007480305,0.00009278367],"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":[0.0001027268,0.00004673425,0.0003245439,0.0001841083,0.00003952958,0.00004784918,0.00005484011,0.0002425347,0.9868483,0.000545211,0.0004356352,0.01112804],"study_design_scores_gemma":[0.000009841396,0.00006412263,0.0009479662,0.00001689812,0.00002286557,0.0001218284,0.00002124771,0.008174471,0.9869976,0.0003862177,0.003211862,0.00002514647],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1905213,0.002003042,0.7959102,0.0003833935,0.0002248035,0.0004257521,0.001779535,0.005759314,0.002992602],"genre_scores_gemma":[0.2548243,0.002895823,0.7298856,0.000507133,0.00007016777,0.001272559,0.003556681,0.001755969,0.005231688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002963461,"threshold_uncertainty_score":0.0156725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02997809467600726,"score_gpt":0.3351265995632179,"score_spread":0.3051485048872106,"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."}}