At higher taxonomic levels (Kingdom, Phylum), most methods, including KNN, Random Forest, and LinearSVC, show moderate effect sizes (Cohen's d 0.3–0.47) and highly significant differences, indicating that metadata contributes substantially to model performance when microbiome signals are coarse.
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Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies.
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