{"id":"W3034902421","doi":"10.1021/acs.jproteome.0c00186","title":"Data Dependent–Independent Acquisition (DDIA) Proteomics","year":2020,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Genome British Columbia; Canada Foundation for Innovation; Ontario Genomics; Genome Canada","keywords":"Computer science; Data acquisition; Workflow; False discovery rate; Classifier (UML); Pipeline (software); Proteomics; Artificial intelligence; Data mining; Pattern recognition (psychology); Chemistry","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":[0.004736351,0.001557837,0.001193048,0.0013203,0.0006900327,0.002404171,0.002372822,0.001274232,0.003107178],"category_scores_gemma":[0.005413648,0.0009020297,0.001053324,0.001221858,0.001306875,0.002934705,0.002981088,0.003263527,0.0025138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006829108,"about_ca_system_score_gemma":0.001488137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003049847,"about_ca_topic_score_gemma":0.0004655503,"domain_scores_codex":[0.9961888,0.0006237709,0.0003056403,0.001235579,0.001392744,0.0002534191],"domain_scores_gemma":[0.9947528,0.001887163,0.0005065867,0.001546374,0.001103059,0.0002039979],"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":[0.0007354293,0.0002779193,0.002108225,0.0009059024,0.0002204746,0.0002208536,0.000154784,0.001702595,0.8432838,0.006908114,0.006918207,0.1365637],"study_design_scores_gemma":[0.00005373066,0.0003196412,0.001637885,0.0000448713,0.000062203,0.0007724169,0.00003714619,0.03438288,0.9355034,0.005609838,0.02145576,0.0001201182],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02980215,0.001323536,0.9546979,0.0004806749,0.0002609395,0.0003694158,0.001428297,0.008523077,0.003114047],"genre_scores_gemma":[0.1715149,0.001499797,0.8138578,0.001508112,0.0001918694,0.0009784275,0.003910168,0.001677697,0.004861305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004736351,"threshold_uncertainty_score":0.02504849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1921019785571602,"score_gpt":0.4275314179323296,"score_spread":0.2354294393751694,"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."}}