Ahmed A. Metwally

The paper identifies rare variant polygenic risk scores as more effective than common variant polygenic risk scores in identifying individuals with outlier phenotypes in common human diseases and complex traits.

This paper introduces a statistical method to identify time intervals of temporal omics biomarkers in longitudinal studies, showing high specificity and sensitivity across simulated and real datasets.

Meta-Signer identifies metagenomic signatures for disease association by aggregating feature rankings from multiple machine learning models.

PopPhy-CNN uses a phylogenetic tree embedded architecture to predict host phenotype from metagenomic data, outperforming traditional and baseline models.

The paper characterizes the dynamic multi-omic molecular response to acute exercise and identifies biomarkers of fitness.