Idan Hekselman

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

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