Zelmina Lubovac-Pilav

This study uses a variational autoencoder trained on healthy human RNA-seq data to predict disease gene expression changes and identify drug targets through latent space arithmetic.

NetAn enhances gene annotation enrichment by integrating network topology, improving identification of disease-related annotations in Multiple Sclerosis.

ComHub predicts hub transcription factors in gene regulatory networks by averaging predictions from multiple network inference methods, showing robust performance on benchmark datasets.

The paper benchmarks disease module identification methods using GWAS and validates a multi-omic module strongly associated with multiple sclerosis risk factors.