Ioannis N. Anastopoulos

The paper presents a deep learning framework to predict nanopore sequencing ionic currents from nucleotide chemical structures and demonstrates its ability to generalize chemical information for modified nucleotides.

This paper develops a deep learning framework to improve clinical drug response prediction by integrating gene expression and drug chemical structures, showing better performance than existing methods.

This paper introduces VEGA, a variational autoencoder with interpretable latent variables inspired by biological networks, demonstrating its ability to model cellular responses and regulatory mechanisms.

The paper introduces LURE, a semi-supervised method to predict cancer drivers using gene expression signatures, and applies it to TCGA data to find new associations with known mutation events.

CODEX2 improves CNV detection sensitivity and reduces false discoveries in whole-exome and targeted sequencing data.