{"id":"W2045117578","doi":"10.1038/nmeth.3255","title":"DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics","year":2015,"lang":"en","type":"article","venue":"Nature Methods","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":695,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Lunenfeld-Tanenbaum Research Institute","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; Canadian Institutes of Health Research","keywords":"Workflow; Computer science; Multiplex; Proteomics; Tandem mass spectrometry; Mass spectrometry; Software; Fragmentation (computing); Data acquisition; Database search engine; Data mining; Computational biology; Chemistry; Chromatography; Bioinformatics; Database; Information retrieval; Biology; Search engine; Programming language","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":[0.0005026348,0.0001916629,0.0002389811,0.00003865585,0.0001325416,0.00005025668,0.0005723297,0.000633844,0.00003900533],"category_scores_gemma":[0.0004082139,0.0001891431,0.00007022611,0.0001397906,0.00005713895,0.0001447282,0.0002515843,0.0008644162,0.000005982631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001263898,"about_ca_system_score_gemma":0.0001054493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003336304,"about_ca_topic_score_gemma":5.880856e-7,"domain_scores_codex":[0.9986862,0.00005659466,0.0002736873,0.0005163812,0.0002400015,0.0002271588],"domain_scores_gemma":[0.997845,0.000610527,0.0002066132,0.0008156369,0.0003851898,0.0001370397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006421226,0.0004910066,0.0003564244,0.0004407459,0.0002575212,0.000007368324,0.0004225469,0.004533911,0.2266515,0.5955649,0.0180052,0.1526268],"study_design_scores_gemma":[0.0004789953,0.00002457143,0.00003018892,0.00003998312,0.00003710625,0.00001537355,0.00006500587,0.01782152,0.1821547,0.7187673,0.08029769,0.0002675756],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002419049,0.000559959,0.9938924,0.0008174122,0.0001301659,0.0006342768,0.0003908897,0.0002305471,0.0009253307],"genre_scores_gemma":[0.01506503,0.00001443112,0.9816936,0.0006628899,0.000443226,0.0004616996,0.001507427,0.00004263114,0.0001091029],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1523592,"threshold_uncertainty_score":0.7713032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09065455223700448,"score_gpt":0.4644961570090743,"score_spread":0.3738416047720698,"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."}}