{"id":"W4405999451","doi":"10.1101/2024.08.26.609661","title":"PhyloMix: Enhancing microbiome-trait association prediction through phylogeny-mixing augmentation","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trait; Microbiome; Association (psychology); Evolutionary biology; Phylogenetics; Mixing (physics); Biology; Computational biology; Data science; Computer science; Genetics; Psychology; Physics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003185931,0.001606598,0.001061126,0.00105284,0.0006166709,0.001045142,0.001668924,0.001219868,0.002694686],"category_scores_gemma":[0.00811381,0.0006272167,0.001540019,0.0008543244,0.0008513871,0.001974213,0.002254717,0.002519711,0.0009912249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005547765,"about_ca_system_score_gemma":0.001083067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003006535,"about_ca_topic_score_gemma":0.003961134,"domain_scores_codex":[0.9988971,0.0004499829,0.00004297226,0.0003823828,0.0001544975,0.00007314375],"domain_scores_gemma":[0.9964743,0.002332204,0.0002399,0.0005091825,0.0003039145,0.0001404912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001498582,0.000562579,0.0326202,0.0004391368,0.0004396522,0.0003118047,0.00039021,0.5621954,0.03455259,0.00542962,0.0109371,0.3506231],"study_design_scores_gemma":[0.00002847075,0.00006094306,0.0008288465,0.00001165667,0.00002178898,0.00003358891,0.000017364,0.9913191,0.003936529,0.002573954,0.001152922,0.00001481309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1710028,0.001490353,0.8054049,0.0009503799,0.0001845877,0.0001562864,0.002204914,0.0167894,0.001816408],"genre_scores_gemma":[0.5358728,0.0003426624,0.4536746,0.000810198,0.0001393648,0.0003716339,0.005868579,0.0006833067,0.002236934],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003185931,"threshold_uncertainty_score":0.01684898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008555885420963933,"score_gpt":0.2350173274939955,"score_spread":0.2264614420730316,"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."}}