{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004263503,0.0002123699,0.0005246568,0.0004069489,0.0003939431,0.0002423813,0.001506553,0.0002348973,0.0002355832],"category_scores_gemma":[0.001332545,0.0001991217,0.0001575414,0.0007773006,0.000179995,0.0007061744,0.001635645,0.001089039,0.000004360397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002289081,"about_ca_system_score_gemma":0.0006931085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002748505,"about_ca_topic_score_gemma":0.00003654772,"domain_scores_codex":[0.9967442,0.0001074955,0.0009471657,0.0008051487,0.0009165573,0.0004794511],"domain_scores_gemma":[0.9946952,0.0004221484,0.000569811,0.002075309,0.001978889,0.0002586568],"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":[0.000961122,0.0005802391,0.001332191,0.0004527644,0.0008902008,0.00005182921,0.00009655394,0.0003219351,0.9818491,0.001026852,0.002261312,0.01017591],"study_design_scores_gemma":[0.00462753,0.0002229625,0.0002935827,0.0003078982,0.00100656,0.0005892105,0.0008748082,0.3568607,0.5721118,0.03377451,0.02857656,0.0007538369],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1015361,0.0006443856,0.8925932,0.00207957,0.00003591875,0.001150858,0.001778242,0.00003247969,0.0001492561],"genre_scores_gemma":[0.1008005,0.001162919,0.8933019,0.00004552562,0.001100542,0.0004037754,0.002089396,0.00006414729,0.001031265],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4097372,"threshold_uncertainty_score":0.8119947,"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."}}