Hussein A. Hejase

The paper benchmarks five machine learning-based variant annotation methods for rare variant association testing across 350,377 UK Biobank participants, finding CADD provides best signal separation and GPN-MSA highest enrichment for loss-of-function intolerant genes.

The paper identifies regulators of inflammatory macrophage states and shows that TNF mRNA m6A modification controls its stability and contributes to disease.

SIA detects and quantifies positive selection using deep learning on the ancestral recombination graph, showing performance comparable to existing methods and identifying novel selection signals in human and bird populations.

The paper introduces a new non-parametric/semi-parametric sequential resampling method for support estimation in biomolecular sequences, showing comparable or better performance than existing methods.

This study introduces FastNet, a new method for inferring phylogenetic networks from genomic data, showing improved efficiency and accuracy over existing methods.

Coal-Miner improves statistical power and type I error control in GWA studies by accounting for local genealogical variation in sample relatedness.