{"id":"W4392663182","doi":"10.1038/s41467-024-46610-3","title":"Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Cancer Institute; Government of Canada; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; College of Engineering, Michigan State University; Michigan State University","keywords":"Mass spectrometry; Deuterium; Chemistry; Tandem mass spectrometry; Hydrogen–deuterium exchange; Workflow; Proteomics; Data acquisition; Combinatorial chemistry; Peptide; Computer science; Chromatography; Database; Physics; Biochemistry; Nuclear physics; Operating system","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00141037,0.0002397628,0.0003864212,0.0005385806,0.0004507407,0.0002167417,0.00516888,0.0004912509,0.001266166],"category_scores_gemma":[0.0002236104,0.0002493466,0.0001781254,0.001972004,0.00008856519,0.0004080681,0.002594256,0.001044661,0.000009532137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002304468,"about_ca_system_score_gemma":0.00009013302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006778858,"about_ca_topic_score_gemma":0.0001652983,"domain_scores_codex":[0.9978847,0.0001370231,0.0004857152,0.0008824962,0.0002616382,0.0003484168],"domain_scores_gemma":[0.988346,0.001083912,0.0002013719,0.01016957,0.0001105487,0.00008856649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002273955,0.0004180035,0.001980106,0.0008512258,0.004855264,0.00001468294,0.0001934274,0.0000937467,0.8679006,0.1068217,0.006632376,0.01021617],"study_design_scores_gemma":[0.0002170802,0.00001873024,0.0004391051,0.00009332845,0.003294409,0.00007804953,0.0001728572,0.8505877,0.02352862,0.008666125,0.1123098,0.0005941628],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006862037,0.03006189,0.9358265,0.005253774,0.0001872482,0.0006336851,0.0112214,0.001685584,0.008267845],"genre_scores_gemma":[0.4916451,0.000512649,0.4921893,0.00008447613,0.0001990999,0.0001049927,0.01510115,0.00003659684,0.0001265547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.850494,"threshold_uncertainty_score":0.9999959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1744756753377625,"score_gpt":0.4302393758593703,"score_spread":0.2557637005216079,"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."}}