{"id":"W4376132442","doi":"10.1093/nar/gkad407","title":"MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data","year":2023,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":584,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Genome Canada","keywords":"Profiling (computer programming); Microbiome; Visualization; Biology; Computational biology; Data science; Human Microbiome Project; Metabolomics; Computer science; Bioinformatics; Data mining; Human microbiome","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007322122,0.0001329052,0.0003622691,0.0008892128,0.0001726227,0.00003577701,0.0003751222,0.00009104636,0.0001545434],"category_scores_gemma":[0.000320626,0.0001101956,0.00006903379,0.002443826,0.0006352834,0.000005686119,0.001267741,0.0001661925,0.00002799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001224055,"about_ca_system_score_gemma":0.00006598997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008410122,"about_ca_topic_score_gemma":0.00009187222,"domain_scores_codex":[0.9983743,0.0001811871,0.000248927,0.0005817065,0.0002738182,0.0003400147],"domain_scores_gemma":[0.998572,0.0001644774,0.00005684024,0.0006807463,0.0004423074,0.00008366325],"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":[0.0000849702,0.00004705784,0.003830826,0.00002261835,0.0024379,0.000004025157,0.00006538704,0.000009542462,0.9490269,0.001105572,0.04234526,0.001019933],"study_design_scores_gemma":[0.001774457,0.0009669916,0.4724602,0.00003293387,0.001430609,0.00001594917,0.005971004,0.006649299,0.122397,0.000736729,0.3868031,0.0007616756],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937029,0.001392003,0.001642907,0.0002822004,0.00006039759,0.0001589176,0.002200383,0.00001189136,0.0005483871],"genre_scores_gemma":[0.9899426,0.00254206,0.001767677,0.00004058545,0.00006484451,0.000009639604,0.004545098,0.00001757202,0.001069957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8266299,"threshold_uncertainty_score":0.4493647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09904488799082163,"score_gpt":0.3842430342342067,"score_spread":0.2851981462433851,"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."}}