{"id":"W4308260692","doi":"10.1101/2022.11.04.515228","title":"Pairing Metagenomics and Metaproteomics to Characterize Ecological Niches and Metabolic Essentiality of gut microbiomes","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Government of Canada; Genome Canada; Ontario Genomics; Ontario Genomics Institute; University of Ottawa","keywords":"Metaproteomics; Metagenomics; Ecological niche; Biology; Computational biology; Microbiome; Niche; Proteome; Ecology; Gene; Evolutionary biology; Bioinformatics; Genetics; Habitat","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":[0.002545917,0.0014731,0.0009508314,0.002327875,0.0005698653,0.001641745,0.0007327138,0.0008983912,0.001256632],"category_scores_gemma":[0.004330322,0.0006795258,0.001853581,0.00144396,0.0005404009,0.001250471,0.001511441,0.001062561,0.0004425402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006274891,"about_ca_system_score_gemma":0.001462025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001585623,"about_ca_topic_score_gemma":0.001947658,"domain_scores_codex":[0.9993137,0.0002211191,0.00003487127,0.0002205315,0.0001452437,0.00006446751],"domain_scores_gemma":[0.9984789,0.0007502108,0.0001911217,0.0002729141,0.0001705569,0.0001362192],"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":[0.002143905,0.001119272,0.2309616,0.001323009,0.003120588,0.0005399339,0.0004192292,0.2261879,0.4238304,0.006421641,0.00285572,0.1010768],"study_design_scores_gemma":[0.00008068767,0.0002422398,0.04948358,0.00003431343,0.0002785481,0.0001723283,0.0001469787,0.8596609,0.07331123,0.01401482,0.002485679,0.00008867717],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5739989,0.0008513589,0.4114596,0.0007242094,0.00008572596,0.0001858745,0.006946111,0.004161654,0.001586535],"genre_scores_gemma":[0.7464206,0.0002953865,0.2476818,0.0001704298,0.00003156732,0.0001410637,0.00450907,0.0003401775,0.0004099262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002545917,"threshold_uncertainty_score":0.01346427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160907937020225,"score_gpt":0.2342151599932256,"score_spread":0.2181243662912031,"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."}}