{"id":"W2194477621","doi":"10.1038/nmeth.3655","title":"MSPLIT-DIA: sensitive peptide identification for data-independent acquisition","year":2015,"lang":"en","type":"letter","venue":"Nature Methods","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":130,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Spinal Cord Injury BC; Sinai Health System; Lunenfeld-Tanenbaum Research Institute","funders":"National Institute of General Medical Sciences; Canadian Institutes of Health Research","keywords":"Identification (biology); Computational biology; Peptide; Chemistry; Biology; Biochemistry","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":[0.003592423,0.001304151,0.0009840014,0.0005856921,0.0008810796,0.001719863,0.00184682,0.005520467,0.003353971],"category_scores_gemma":[0.003936994,0.0009904546,0.0003442491,0.0004564917,0.002276235,0.002049641,0.001754787,0.01079788,0.008304268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001241658,"about_ca_system_score_gemma":0.0007579929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002713561,"about_ca_topic_score_gemma":0.0009822994,"domain_scores_codex":[0.9973781,0.0005137238,0.0001497871,0.0003663161,0.001364276,0.0002278266],"domain_scores_gemma":[0.998471,0.0007323118,0.0001252067,0.0001981903,0.0002877693,0.0001854745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007055092,0.0002460836,0.0005799133,0.0006976216,0.0000679131,0.001518022,0.0001696263,0.0004158933,0.1774376,0.02938244,0.5958213,0.1929581],"study_design_scores_gemma":[0.0002503605,0.0004084831,0.0006494426,0.00007786057,0.00003209161,0.004113448,0.00003431055,0.0149649,0.239569,0.02175053,0.7179813,0.0001681929],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01943864,0.03232304,0.5142259,0.325684,0.04748416,0.001084278,0.001524303,0.01626014,0.04197551],"genre_scores_gemma":[0.2069462,0.02123511,0.3977829,0.2264023,0.03166443,0.002921438,0.002144008,0.002138951,0.1087646],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005520467,"threshold_uncertainty_score":0.01899874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05070079411617558,"score_gpt":0.4229341241777745,"score_spread":0.3722333300615989,"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."}}