{"id":"W3175073006","doi":"10.1021/acs.jproteome.1c00123","title":"DIAproteomics: A Multifunctional Data Analysis Pipeline for Data-Independent Acquisition Proteomics and Peptidomics","year":2021,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Boehringer Ingelheim Fonds","keywords":"Pipeline (software); Computer science; Workflow; Data mining; Data acquisition; Software; False discovery rate; Pairwise comparison; Proteomics; Database; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.006437123,0.003063832,0.001769891,0.003673565,0.001533061,0.003890143,0.002866493,0.001408151,0.01600415],"category_scores_gemma":[0.007607442,0.001674729,0.00193902,0.002455185,0.00107481,0.003187157,0.005124062,0.004141494,0.01241439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001273076,"about_ca_system_score_gemma":0.004397302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001847932,"about_ca_topic_score_gemma":0.001919245,"domain_scores_codex":[0.9972772,0.0003013412,0.0003056907,0.0008317378,0.000969308,0.0003148625],"domain_scores_gemma":[0.9967794,0.001050254,0.0003461877,0.0006744771,0.0007805714,0.0003691658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005222277,0.000621606,0.009773537,0.003229817,0.0009911949,0.0009706201,0.001130359,0.006246349,0.2510783,0.01726885,0.3879572,0.3155099],"study_design_scores_gemma":[0.0007814313,0.0005031165,0.01259473,0.0004349407,0.000323662,0.001402555,0.0001891484,0.125989,0.3289771,0.04584723,0.4819228,0.001034266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00783817,0.0007143888,0.615126,0.0006624885,0.0003824471,0.0008686818,0.04302699,0.3257184,0.005662572],"genre_scores_gemma":[0.05049193,0.001072124,0.788059,0.001518222,0.0003000409,0.003855775,0.1096026,0.03721356,0.00788675],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01600415,"threshold_uncertainty_score":0.05353922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1625535362056212,"score_gpt":0.4374129983407201,"score_spread":0.2748594621350989,"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."}}