{"id":"W4412654241","doi":"10.1038/s42003-025-08515-9","title":"A systematic benchmark of integrative strategies for microbiome-metabolome data","year":2025,"lang":"en","type":"article","venue":"Communications Biology","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Centre hospitalier de l'Université Laval","funders":"Fogarty International Center","keywords":"Metagenomics; Metabolome; Microbiome; Data integration; Automatic summarization; Data science; Computer science; Metabolomics; Computational biology; Benchmark (surveying); Identification (biology); Biology; Data mining; Bioinformatics; Artificial intelligence; Ecology","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":[0.06652194,0.003653841,0.002231854,0.006767738,0.001990911,0.00383068,0.00412984,0.001904678,0.00187185],"category_scores_gemma":[0.1428893,0.001085661,0.003933873,0.006480574,0.001446442,0.004338096,0.005785867,0.002227416,0.0007926392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002188386,"about_ca_system_score_gemma":0.006435522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007584771,"about_ca_topic_score_gemma":0.009296134,"domain_scores_codex":[0.9685987,0.02009381,0.002862812,0.003662936,0.004231167,0.0005506141],"domain_scores_gemma":[0.9133317,0.06027811,0.002709837,0.01208242,0.01054711,0.001050816],"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":[0.002411686,0.001223763,0.05772774,0.007793185,0.009998126,0.0005619832,0.001095064,0.4276423,0.01645755,0.02289753,0.01553889,0.4366523],"study_design_scores_gemma":[0.0005528914,0.001127654,0.01323394,0.0008991335,0.001443109,0.0004595023,0.0006616881,0.9250925,0.01791004,0.02520972,0.01319214,0.0002177408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1965591,0.01148198,0.7628269,0.001745514,0.000447224,0.002935952,0.008433768,0.01021438,0.005355196],"genre_scores_gemma":[0.1980228,0.001777684,0.7792091,0.0003538594,0.00008170815,0.001969005,0.01643794,0.00164628,0.0005016135],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06652194,"threshold_uncertainty_score":0.3518059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03283709284030424,"score_gpt":0.3569962657744645,"score_spread":0.3241591729341602,"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."}}