{"id":"W4311575706","doi":"10.1093/bioinformatics/btac809","title":"Efficient computation of contributional diversity metrics from microbiome data with <i>FuncDiv</i>","year":2022,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Dalhousie University; McGill Genome Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"License; Metagenomics; Computer science; Microbiome; ENCODE; Diversity (politics); Function (biology); Alpha diversity; Data mining; Computational biology; Data science; Biology; Bioinformatics; Biodiversity; Genetics; Gene; Ecology","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":[0.002634276,0.002644655,0.001328711,0.002820793,0.001016842,0.003126844,0.002469438,0.0009369115,0.009161694],"category_scores_gemma":[0.01465547,0.0008748423,0.002231926,0.002620809,0.001088646,0.00305809,0.003230525,0.001553322,0.007882582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001328478,"about_ca_system_score_gemma":0.002336298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00354481,"about_ca_topic_score_gemma":0.005494885,"domain_scores_codex":[0.9987268,0.0002601311,0.0001167673,0.0004024665,0.0003473065,0.0001465447],"domain_scores_gemma":[0.9965605,0.00163622,0.0003321389,0.0006750686,0.0005822819,0.0002137785],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001703024,0.0004223101,0.05636946,0.003062611,0.001134602,0.0008476697,0.001520707,0.1315078,0.0610709,0.03437393,0.1879996,0.5199875],"study_design_scores_gemma":[0.0002406137,0.0002406222,0.01227837,0.0002453494,0.0001280422,0.0007786821,0.0004210949,0.7882537,0.04575679,0.08263841,0.06877641,0.0002418466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06236225,0.00105113,0.6962617,0.0009183036,0.0002717976,0.0002195054,0.03228854,0.1989668,0.007659926],"genre_scores_gemma":[0.1618452,0.000496334,0.7588304,0.0005350183,0.0001545801,0.0005980998,0.05925357,0.01564959,0.002637056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009161694,"threshold_uncertainty_score":0.03064889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919680741520582,"score_gpt":0.2424538403965927,"score_spread":0.2232570329813868,"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."}}