{"id":"W3106125405","doi":"10.1101/753996","title":"MetaLab 2.0 enables accurate post-translational modifications profiling in metaproteomics","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministero dello Sviluppo Economico; Ontario Ministry of Economic Development and Innovation; Government of Canada; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Ontario Genomics Institute; University of Ottawa","keywords":"Metaproteomics; Microbiome; Computational biology; Workflow; Profiling (computer programming); Posttranslational modification; Proteomics; Identification (biology); Biology; Human microbiome; Human Microbiome Project; Computer science; Function (biology); Metagenomics; Bioinformatics; Data science; Genetics; Ecology; Biochemistry; Gene","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":[0.002576905,0.001697928,0.001040479,0.001492979,0.0005570594,0.001974559,0.001148622,0.0009400777,0.003050354],"category_scores_gemma":[0.003094737,0.0009561887,0.000911854,0.0009556072,0.000411747,0.001290216,0.001773296,0.001203265,0.002524745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003970282,"about_ca_system_score_gemma":0.001098633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000899344,"about_ca_topic_score_gemma":0.001121222,"domain_scores_codex":[0.9989844,0.0001897573,0.00008853869,0.0003111686,0.0003397584,0.00008642784],"domain_scores_gemma":[0.9989557,0.0003798986,0.0001831924,0.0002027711,0.0001903649,0.00008807248],"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":[0.002664219,0.0002805721,0.009771483,0.000854804,0.0005259644,0.0003042122,0.0002413482,0.008176765,0.8589125,0.002814332,0.01155895,0.1038949],"study_design_scores_gemma":[0.0001580394,0.0002585251,0.00892355,0.00006201532,0.0001088224,0.0004678054,0.00008088785,0.3181527,0.6486124,0.004220918,0.01880047,0.0001539543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2578713,0.001097574,0.6131045,0.0004603153,0.0002090309,0.0002600447,0.00969555,0.1143199,0.002981791],"genre_scores_gemma":[0.2784423,0.0004514968,0.7036239,0.0003151267,0.00006094503,0.0005363961,0.008316804,0.006009747,0.002243317],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003050354,"threshold_uncertainty_score":0.01362813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02020319752892605,"score_gpt":0.2540362872716917,"score_spread":0.2338330897427656,"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."}}