Juman Jubran

The paper characterizes the expression and essentiality of protein clearance systems across human tissues and cell types, revealing tissue-specific regulation and a core set of stably expressed genes.

The paper uses snRNA-seq to analyze human subcutaneous and visceral adipose tissue, identifying classical and specialized adipocyte subtypes and depot-specific patterns.

The paper introduces WGAND, a network-based anomaly detection method for weighted graphs, which identifies proteins with major roles in human tissues by analyzing protein interaction networks.

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.