{"id":"W4200570320","doi":"10.1016/j.csbj.2021.12.012","title":"Inferring early-life host and microbiome functions by mass spectrometry-based metaproteomics and metabolomics","year":2021,"lang":"en","type":"review","venue":"Computational and Structural Biotechnology Journal","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Cumming School of Medicine, University of Calgary; Alberta Children's Hospital Research Institute; Fundação Oswaldo Cruz; Norges Forskningsråd; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; W. Garfield Weston Foundation; Canadian Institutes of Health Research; Sick Kids Foundation","keywords":"Metaproteomics; Microbiome; Computational biology; Metabolomics; Biology; Gut microbiome; Metagenomics; Human microbiome; Identification (biology); Omics; Bioinformatics; Ecology; Gene; Genetics","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":[0.002559562,0.001848613,0.002917093,0.003922909,0.0002472612,0.001950882,0.001863808,0.001786652,0.001935057],"category_scores_gemma":[0.003178243,0.0006114158,0.001327688,0.003162666,0.0006598479,0.001995055,0.001161095,0.001668102,0.001811529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006770593,"about_ca_system_score_gemma":0.001446196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001185902,"about_ca_topic_score_gemma":0.001724142,"domain_scores_codex":[0.9993322,0.0001403432,0.00004809273,0.0001806472,0.000256482,0.00004208892],"domain_scores_gemma":[0.9980932,0.001205353,0.000174263,0.00007889535,0.0003564651,0.00009177395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001040582,0.00007749247,0.001562027,0.02707985,0.0008173554,0.0002105034,0.00006358061,0.002099399,0.008488754,0.006543999,0.01121004,0.941743],"study_design_scores_gemma":[0.00004079279,0.0003193399,0.008923643,0.009527553,0.001680003,0.002621157,0.0002422291,0.004428905,0.01257262,0.01715454,0.9422835,0.0002058183],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005039684,0.9930489,0.004587479,0.0003402756,0.0002003402,0.0000155912,0.0001666985,0.00005347403,0.001083255],"genre_scores_gemma":[0.003038779,0.9900239,0.005761986,0.0002463254,0.0001803367,0.00002459358,0.0002543502,0.00001852304,0.0004512407],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003922909,"threshold_uncertainty_score":0.01353639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0135740744053331,"score_gpt":0.2740460071332131,"score_spread":0.2604719327278801,"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."}}