{"id":"W4413055840","doi":"10.1177/15578666251364292","title":"Enhanced Interpretable Neural Network Approach for Unified Batch Effect Mitigation and Disease Classification Using Cross-Cohort Microbiome Profiles","year":2025,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Oral microbiology and periodontitis research","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Manitoba","funders":"","keywords":"Microbiome; Cohort; Artificial neural network; Disease; Artificial intelligence; Computer science; Machine learning; Computational biology; Biology; Mathematics; Medicine; Bioinformatics; Statistics; Internal medicine","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":[0.003651225,0.001500766,0.001657481,0.001118327,0.000573413,0.001350611,0.001991314,0.001567427,0.002385866],"category_scores_gemma":[0.004853019,0.0005580175,0.001937094,0.0007191553,0.0004188847,0.0008014712,0.001542995,0.0026517,0.0006994642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008963781,"about_ca_system_score_gemma":0.001535866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01202673,"about_ca_topic_score_gemma":0.01214541,"domain_scores_codex":[0.9989859,0.000381521,0.00007329354,0.0003084891,0.0001171351,0.0001336044],"domain_scores_gemma":[0.9980019,0.001164,0.0001709476,0.0001370452,0.000396125,0.0001299173],"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":[0.001775901,0.0009555884,0.04118665,0.0003603781,0.001228605,0.0008191129,0.0002694275,0.5784493,0.007141757,0.002839821,0.008858574,0.356115],"study_design_scores_gemma":[0.00001725971,0.00006695488,0.001205626,0.00001345683,0.00004275556,0.00003485077,0.00001560164,0.9965068,0.0004181339,0.001283526,0.0003830442,0.00001194642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.135696,0.00324397,0.8521247,0.001981637,0.0003701622,0.0002332608,0.001838781,0.002680493,0.001830931],"genre_scores_gemma":[0.828555,0.00104297,0.15658,0.001137291,0.0004355164,0.0006105906,0.005683835,0.0002058238,0.005748975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01202673,"threshold_uncertainty_score":0.02391344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01939396327145821,"score_gpt":0.3546994212512898,"score_spread":0.3353054579798316,"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."}}