{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003556657,0.001233631,0.001081994,0.001364958,0.0005205589,0.001739321,0.001291607,0.001027951,0.006646469],"category_scores_gemma":[0.004781867,0.0008434184,0.0008233446,0.001051835,0.0009512038,0.00245713,0.002604041,0.002206838,0.002826547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006619475,"about_ca_system_score_gemma":0.001094408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004934615,"about_ca_topic_score_gemma":0.001758453,"domain_scores_codex":[0.9980123,0.000319951,0.0001067343,0.0007285195,0.0006968381,0.0001357098],"domain_scores_gemma":[0.9952662,0.00217109,0.0005806052,0.001006442,0.0008254882,0.000150062],"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":[0.0004807209,0.0001465712,0.001740333,0.0003808491,0.0001097295,0.00009996764,0.000134891,0.001328261,0.9262415,0.003056467,0.002171467,0.06410925],"study_design_scores_gemma":[0.00005532502,0.000230115,0.004618387,0.00005813428,0.00006208826,0.0005014635,0.00005116396,0.03147046,0.9393405,0.002287649,0.02121659,0.0001081236],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.085379,0.001478864,0.8975372,0.0003252082,0.0001739297,0.0002650283,0.001503732,0.009452104,0.003884929],"genre_scores_gemma":[0.1463973,0.001154212,0.8395746,0.0009141646,0.0001083336,0.0007333672,0.003150925,0.002868201,0.005098934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006646469,"threshold_uncertainty_score":0.02223468,"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."}}