{"id":"W2461711360","doi":"10.1186/s40168-016-0176-z","title":"MetaPro-IQ: a universal metaproteomic approach to studying human and mouse gut microbiota","year":2016,"lang":"en","type":"article","venue":"Microbiome","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":231,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministero dello Sviluppo Economico; Government of Canada; Canadian Institutes of Health Research; Genome Canada; Ontario Genomics; Ontario Ministry of Economic Development and Innovation; Ontario Genomics Institute","keywords":"Metaproteomics; Biology; Metagenomics; Gut flora; Computational biology; Lipidome; Microbiology; Bioinformatics; Genetics; Gene; Immunology; Lipidomics","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":[0.002612215,0.001739225,0.001341691,0.003246522,0.0007205695,0.001343448,0.001493117,0.001037015,0.001594932],"category_scores_gemma":[0.001062801,0.0008844226,0.001612822,0.001214705,0.0006654191,0.001008479,0.002336626,0.001922426,0.0008754411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000447508,"about_ca_system_score_gemma":0.0008846489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004143428,"about_ca_topic_score_gemma":0.0008961086,"domain_scores_codex":[0.9983589,0.0002242884,0.0001687103,0.0005899793,0.000503213,0.0001548685],"domain_scores_gemma":[0.9992867,0.0001034079,0.0002019173,0.0001341822,0.0001449432,0.0001288404],"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":[0.0003820474,0.0001134408,0.004096628,0.0004099405,0.0001717205,0.00009451227,0.00009800354,0.0005566371,0.9730815,0.0005001564,0.0006691364,0.01982618],"study_design_scores_gemma":[0.0001115144,0.0009553849,0.01949986,0.0001236759,0.0002913773,0.001087365,0.0001030385,0.02306931,0.9323591,0.001354184,0.02090729,0.0001378404],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2959755,0.005079044,0.6723638,0.0005009864,0.0002215378,0.001431401,0.01070185,0.01120328,0.002522616],"genre_scores_gemma":[0.2047397,0.002007101,0.7788533,0.0004964878,0.0000689099,0.002643551,0.008222356,0.001158213,0.001810364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003246522,"threshold_uncertainty_score":0.01381487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698193715608017,"score_gpt":0.2470030957009087,"score_spread":0.2300211585448285,"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."}}