{"id":"W4311860120","doi":"10.1002/pmic.202200116","title":"An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis","year":2022,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Health Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Health Canada; Ministero dello Sviluppo Economico; Government of Canada; Ontario Ministry of Economic Development and Innovation; Genome Canada","keywords":"Proteomics; Metaproteomics; Quantitative proteomics; Computational biology; Tandem mass tag; Chromatography; Chemistry; Biology; Bioinformatics; 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":[0.002033395,0.001331595,0.0007661584,0.001057994,0.0006216159,0.001013832,0.001052351,0.000970123,0.001249195],"category_scores_gemma":[0.00131695,0.0007242807,0.0008298089,0.001094151,0.0005247446,0.001026658,0.001046997,0.002108361,0.001680715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006116804,"about_ca_system_score_gemma":0.0009053641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006074173,"about_ca_topic_score_gemma":0.001389037,"domain_scores_codex":[0.9983404,0.0002946767,0.0001197924,0.0003900118,0.0007358685,0.0001192151],"domain_scores_gemma":[0.9993715,0.0001017443,0.0001507876,0.0001241035,0.0001960268,0.00005593404],"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":[0.0000292868,0.00003018495,0.0001150144,0.00005976535,0.00001229009,0.00003239945,0.00001411441,0.0001686667,0.9945161,0.0002453903,0.0002444271,0.004532288],"study_design_scores_gemma":[0.00001560432,0.0001766673,0.001188258,0.00001567798,0.00003349074,0.000374576,0.00002303195,0.005300148,0.9830496,0.000410801,0.009373088,0.00003909942],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07242072,0.001591216,0.9196784,0.0004690728,0.0002948159,0.0007172143,0.00117447,0.001751876,0.001902119],"genre_scores_gemma":[0.1141749,0.002007221,0.875016,0.0004255447,0.0001394555,0.001109059,0.002741653,0.0004488713,0.003937232],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002033395,"threshold_uncertainty_score":0.01075375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01900586111100613,"score_gpt":0.2665145741508964,"score_spread":0.2475087130398902,"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."}}