{"id":"W4380153583","doi":"10.1038/s41467-023-39149-2","title":"Revealing proteome-level functional redundancy in the human gut microbiome using ultra-deep metaproteomics","year":2023,"lang":"en","type":"article","venue":"Nature Communications","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Heart, Lung, and Blood Institute; National Institute on Aging; National Institute of Allergy and Infectious Diseases; Ministero dello Sviluppo Economico; Ontario Genomics; National Institutes of Health; Government of Canada; Genome Canada; Western Canada Research Grid; Ontario Ministry of Economic Development and Innovation; Compute Canada; Ontario Genomics Institute; University of Ottawa","keywords":"Metaproteomics; Redundancy (engineering); Computer science; Proteome; Artificial intelligence; Algorithm; Metagenomics; Bioinformatics; Biology; Genetics; 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":[0.0006377985,0.0004383798,0.000437716,0.00116513,0.0002772807,0.0005859229,0.000344805,0.0004763351,0.0005979633],"category_scores_gemma":[0.0008875867,0.0003164936,0.0005381843,0.0007760611,0.0003508914,0.0007020038,0.001002013,0.0006143589,0.0001799204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002779997,"about_ca_system_score_gemma":0.0002604597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007574903,"about_ca_topic_score_gemma":0.001201385,"domain_scores_codex":[0.9997175,0.00007532731,0.00001184786,0.00008758494,0.00006930852,0.00003840549],"domain_scores_gemma":[0.9996355,0.0001306977,0.0001121197,0.00004916031,0.00003299303,0.00003947401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005691939,0.000158465,0.08334413,0.0006291741,0.0007853055,0.0004279752,0.0003024345,0.01588829,0.8410862,0.003261858,0.0009660772,0.05258088],"study_design_scores_gemma":[0.00005152446,0.0005375366,0.3464955,0.0001528216,0.0007117034,0.002006633,0.0005930234,0.4068829,0.2099484,0.02743329,0.005006521,0.0001800522],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9539595,0.002357724,0.04121271,0.0003128482,0.00001800904,0.00002270793,0.001134139,0.0001927745,0.0007897532],"genre_scores_gemma":[0.9717157,0.0008296932,0.02623816,0.0001600381,0.00001516487,0.00002503323,0.0008347034,0.00002622692,0.0001551659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00116513,"threshold_uncertainty_score":0.003373027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08327292138297734,"score_gpt":0.3566400011096323,"score_spread":0.273367079726655,"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."}}