{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001920518,0.0001616295,0.0002978051,0.000149484,0.0002308463,0.0001449003,0.002030725,0.0001861767,0.0007394977],"category_scores_gemma":[0.0005285959,0.0001464972,0.00009412517,0.00040334,0.0001190306,0.0005608381,0.0009040094,0.001789894,0.00008002984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002002442,"about_ca_system_score_gemma":0.000370657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009200872,"about_ca_topic_score_gemma":0.000001725823,"domain_scores_codex":[0.9969892,0.0000922711,0.0006919304,0.000412151,0.001349489,0.0004649382],"domain_scores_gemma":[0.9974782,0.0001096567,0.0004307024,0.0008393602,0.0007444722,0.0003975935],"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":[0.0002661045,0.0001337053,0.0003356933,0.0001785258,0.00004649952,0.00007962576,0.0001031807,0.00004413726,0.9932033,0.0005517255,0.002269831,0.002787713],"study_design_scores_gemma":[0.001119393,0.0003213585,0.00006590314,0.000177987,0.00002591405,0.0002247233,0.0003536774,0.002490133,0.9583395,0.01211195,0.02447991,0.0002895631],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4972103,0.001190744,0.4597636,0.02757833,0.00009868629,0.003091962,0.0008145141,0.0002490333,0.01000291],"genre_scores_gemma":[0.8049568,0.0005026321,0.1921259,0.0001408384,0.00153387,0.0001745487,0.00006513009,0.0000733984,0.0004268963],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3077466,"threshold_uncertainty_score":0.809698,"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."}}