Esti Yeger‐Lotem

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.

The paper identifies affected cell types for 714 hereditary diseases using single-cell data from human and mouse tissues.

The paper maps cellular changes in the aging human brain and identifies altered multicellular communities in Alzheimer's disease using single nucleus RNA-seq and bulk RNA-seq data.

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 quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

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 investigates how paralog imbalances contribute to tissue-selective hereditary diseases by analyzing gene expression across multiple tissues.