Omer Basha

This paper introduces a tissue-aware machine learning framework to improve understanding and diagnosis of Mendelian and rare diseases by leveraging tissue-specific gene mechanisms.

The paper analyzes chaperone expression across human tissues, revealing a core set of universally expressed chaperones and tissue-specific variants that contribute to disease susceptibility.

The paper evaluates differential network analysis methods on human tissue interactomes to identify tissue-selective biological processes and disease-related genes.

The paper presents HuRI, a comprehensive human protein-protein interaction map, and demonstrates its utility in understanding cellular function and disease mechanisms.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.