Andrey Alexeyenko

This paper adapts Haralick texture analysis to biological networks to generate pathway-level features from gene-level omics data, showing comparable performance to existing methods and potential for drug response prediction.

The paper introduces NEAdriver, a method that combines network analysis and mutation co-occurrence to identify rare cancer drivers in individual tumor genomes, revealing previously undetected driver events.

The paper identifies a Shh/Gli-driven three-node timer motif that controls the sequential generation of motor neurons and serotonergic neurons in the brainstem.