{"id":"W2342644652","doi":"10.1016/j.jprot.2016.04.042","title":"Data Independent Acquisition analysis in ProHits 4.0","year":2016,"lang":"en","type":"article","venue":"Journal of Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research; Institute for Research in Immunology and Cancer; Sinai Health System; Université de Montréal; Lunenfeld-Tanenbaum Research Institute","funders":"National Center for Research Resources; National Institute of General Medical Sciences; Genome Canada; Canadian Institutes of Health Research; National Institutes of Health; Government of Canada; Ministry of Education - Singapore","keywords":"Computer science; Data science","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":[0.004777058,0.004310472,0.003287413,0.002959321,0.001294961,0.004594598,0.003507021,0.00153776,0.1039995],"category_scores_gemma":[0.00727471,0.001676054,0.001947268,0.002234947,0.00144606,0.001999059,0.001787684,0.00397874,0.03781391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066846,"about_ca_system_score_gemma":0.003496156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002866861,"about_ca_topic_score_gemma":0.003452582,"domain_scores_codex":[0.9976908,0.0003053081,0.0003545262,0.0007498448,0.0005849053,0.0003145512],"domain_scores_gemma":[0.9973531,0.0008545443,0.0002220281,0.000541412,0.0008578619,0.000171173],"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":[0.009988946,0.0007485339,0.005963844,0.008151473,0.001979205,0.001402229,0.002304263,0.00432044,0.1556594,0.02395757,0.5511714,0.2343528],"study_design_scores_gemma":[0.001419383,0.0006123067,0.009695512,0.001028196,0.0006431948,0.001502887,0.0003999727,0.0761122,0.3722816,0.02638956,0.5088355,0.001079635],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01440937,0.0008907471,0.463078,0.000587642,0.001220144,0.002127043,0.05198094,0.4507755,0.01493068],"genre_scores_gemma":[0.06243242,0.000910242,0.7203167,0.001698985,0.0005047115,0.01401578,0.05584284,0.1143075,0.02997083],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1039995,"threshold_uncertainty_score":0.3479128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0265448720886555,"score_gpt":0.3123517120773499,"score_spread":0.2858068399886944,"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."}}