{"id":"W4205578613","doi":"10.1021/acs.analchem.1c03338","title":"BoxCar and Library-Free Data-Independent Acquisition Substantially Improve the Depth, Range, and Completeness of Label-Free Quantitative Proteomics","year":2022,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Quantitative proteomics; Proteomics; Label-free quantification; Data acquisition; Chemistry; Dynamic range; Replicate; Range (aeronautics); Computer science; Data mining; Biological system; Computational biology; Statistics","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":[0.0001946147,0.0001805477,0.0002614225,0.00001550048,0.0002600218,0.00005064443,0.001407592,0.00009154847,0.0002942057],"category_scores_gemma":[0.0001269144,0.0001580499,0.00004271736,0.0001565836,0.0003087963,0.0001601897,0.002943672,0.0004173556,4.11996e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003109612,"about_ca_system_score_gemma":0.00006275001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002574807,"about_ca_topic_score_gemma":0.000004483984,"domain_scores_codex":[0.9986479,0.0000199746,0.0003485722,0.0004848106,0.0002859639,0.0002127937],"domain_scores_gemma":[0.9976938,0.0001769298,0.0002051444,0.001780646,0.00005234411,0.0000910812],"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":[0.0004404009,0.0003072628,0.003538003,0.0006203397,0.0001997593,0.00002042742,0.00016296,0.00003016409,0.9629091,0.02847515,0.002336,0.0009604455],"study_design_scores_gemma":[0.00248629,0.0000927029,0.0003392085,0.00005339699,0.0002879367,0.00008640916,0.001397199,0.02840019,0.8838795,0.07928471,0.0030878,0.0006046591],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9792871,0.0006999113,0.007185533,0.002695442,0.00001312884,0.0004495199,0.003600071,0.0001294543,0.005939859],"genre_scores_gemma":[0.9620345,0.0002016949,0.03630737,0.0001470571,0.00007757526,0.0001553974,0.0005413658,0.00004702405,0.0004879977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07902958,"threshold_uncertainty_score":0.644509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03099711744504522,"score_gpt":0.2876431417184596,"score_spread":0.2566460242734144,"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."}}