A new study introduces the Proteome-Wide Association Study Hub, an innovative and powerful tool designed to explore gene-disease connections across ninety-nine common diseases. Leveraging machine learning and statistical models, the platform (PWAS Hub) identifies genes linked to specific conditions, with separate analyses for male and female subjects as well as inheritance patterns. This accessible resource is set to advance personalized medicine by providing valuable genetic insights to clinicians, researchers, and the public.
A new study led by Professor Michal Linial from the Department of Biological Chemistry at the Hebrew University of Jerusalem, in collaboration with Guy Kelman from The Jerusalem Center for Personalized Computational Medicine, Roei Zucker from The Rachel and Selim Benin School of Computer Science and Engineering, and Nadav Brandes from the University of California (currently NYU, New York), introduces an innovative tool for exploring gene-disease connections: the PWAS Hub. This resource is based on the novel approach of the proteome-wide association study (PWAS), which complements traditional genetic analysis methods like the genome-wide association study (GWAS) by focusing on the effects of genetic variations on the biochemical function of all protein-coding genes. The PWAS Hub is designed to be accessible to clinicians, researchers, and the public, offering an interactive platform to explore gene-disease associations and analyze sex-specific genetic e.