{"id":"W2802799838","doi":"10.1021/acs.jproteome.8b00072","title":"Parsing and Quantification of Raw Orbitrap Mass Spectrometer Data Using RawQuant","year":2018,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"BC Cancer Foundation; Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Orbitrap; Computer science; Parsing; Isobaric labeling; Isobaric process; Raw data; Metadata; Mass spectrometry; Shotgun proteomics; Proteome; Identification (biology); Data mining; File format; Quantitative proteomics; Database; Chromatography; Proteomics; Chemistry; Artificial intelligence; Bioinformatics; Tandem mass spectrometry; Biology; 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":[0.002129276,0.00008582445,0.0002143618,0.0002285541,0.0001775914,0.00005700391,0.0005726013,0.00008613319,0.0001230984],"category_scores_gemma":[0.0002725762,0.00007321357,0.00003840613,0.0003303,0.0003671872,0.0002940162,0.0001987248,0.000522489,0.000002542312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008444764,"about_ca_system_score_gemma":0.000162825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001973057,"about_ca_topic_score_gemma":0.000001044605,"domain_scores_codex":[0.9984527,0.00006081951,0.0005067263,0.0002196243,0.0005016654,0.0002584593],"domain_scores_gemma":[0.9980555,0.0001167533,0.0003961617,0.0006554272,0.0006793297,0.00009683223],"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":[0.00008739471,0.00004756498,0.000203411,0.0001017322,0.0000179493,0.000003389918,0.0000483612,0.000002342536,0.9973922,0.0005053531,0.00008678581,0.001503494],"study_design_scores_gemma":[0.0002356591,0.0001273302,0.00004371746,0.0002201846,0.00001335255,0.00008388203,0.0001116145,0.003812392,0.9810102,0.01268942,0.00157388,0.00007839257],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7530631,0.0002689571,0.245276,0.0005601271,0.00001874103,0.0002729958,0.00002078495,0.00001125055,0.0005080531],"genre_scores_gemma":[0.7200245,0.00015977,0.2794181,0.000002830212,0.0003153608,0.000006320205,0.00000412724,0.00001662936,0.00005234854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03414213,"threshold_uncertainty_score":0.2985563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3041312187879838,"score_gpt":0.4816947854562654,"score_spread":0.1775635666682816,"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."}}