{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002250964,0.0002102462,0.0002114971,0.00002452015,0.0001993373,0.00006388107,0.0006486075,0.0002315269,0.000883114],"category_scores_gemma":[0.0001390707,0.0002182021,0.00006410474,0.0003134543,0.00009437794,0.0001657588,0.0003248127,0.0003222435,0.0001429311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001336202,"about_ca_system_score_gemma":0.00004037858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003285586,"about_ca_topic_score_gemma":0.000002006906,"domain_scores_codex":[0.9981934,0.00000775703,0.0003755006,0.0007431314,0.0003360594,0.0003441901],"domain_scores_gemma":[0.9982027,0.0001001454,0.0001450314,0.001332326,0.00007051826,0.0001492646],"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":[0.00002346495,0.0001007402,0.0003205617,0.0001486598,0.0000203202,0.000002722146,0.0000106913,0.0000452668,0.9945089,0.00013457,0.003165729,0.001518327],"study_design_scores_gemma":[0.0003160177,0.000007327479,0.00005528322,0.00005765476,0.00006415515,0.00001127056,0.0000900646,0.1527206,0.8244876,0.005372991,0.0164001,0.0004170067],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7309023,0.0001547837,0.2540717,0.002774164,0.00002702666,0.0005346527,0.002849045,0.001941597,0.006744772],"genre_scores_gemma":[0.9775595,0.0001550013,0.005872488,0.00004120286,0.00009807305,0.0002227317,0.008756308,0.00005340395,0.007241279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2481992,"threshold_uncertainty_score":0.9669479,"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."}}