{"id":"W2979612020","doi":"10.1101/802231","title":"DDIA: data dependent-independent acquisition proteomics - DDA and DIA in a single LC-MS/MS run","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto; Novelis (Canada); University of Waterloo","funders":"Genome Canada","keywords":"Computer science; Workflow; Pipeline (software); Data acquisition; Classifier (UML); Data extraction; Data mining; Artificial intelligence; Pattern recognition (psychology); Database; 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":[0.003576078,0.001497525,0.001229606,0.002297053,0.0009503895,0.002973165,0.001649291,0.001080308,0.01040425],"category_scores_gemma":[0.004142534,0.001131872,0.001062744,0.001158886,0.0009924325,0.00202019,0.002335837,0.003409145,0.005919217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008910126,"about_ca_system_score_gemma":0.001401157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004446577,"about_ca_topic_score_gemma":0.0007445968,"domain_scores_codex":[0.9976479,0.0002918507,0.0001844707,0.0009471323,0.0007542633,0.000174409],"domain_scores_gemma":[0.9974111,0.0008795537,0.0002917989,0.0007481981,0.0005149985,0.0001544555],"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":[0.00158673,0.0003079888,0.004953066,0.0008003607,0.0002906454,0.0003844557,0.0001870708,0.002230159,0.8300539,0.005943963,0.02092085,0.1323408],"study_design_scores_gemma":[0.00007833532,0.0002145167,0.004410377,0.00004607811,0.00007376559,0.0006718697,0.00005875096,0.04037945,0.9211516,0.004205931,0.02857916,0.000130155],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04777489,0.0008048187,0.8919214,0.000917058,0.0005873266,0.0006770631,0.006876647,0.04421607,0.006224619],"genre_scores_gemma":[0.1467676,0.0005228039,0.8311803,0.001518768,0.0001680651,0.001360455,0.007018883,0.004492694,0.006970399],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01040425,"threshold_uncertainty_score":0.03480572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01911504708846348,"score_gpt":0.24412254039454,"score_spread":0.2250074933060765,"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."}}