Thai-Hoang Pham

The paper introduces a deep learning framework, CIGER, for predicting gene expression rankings induced by chemicals and applies it to pancreatic cancer drug repurposing, showing improved performance over existing methods.

FAME generates novel molecules with desired biological activity by learning from gene expression data using a conditional variational autoencoder and fragment-based generation.

This paper introduces DeepCE, a deep learning framework for chemical-induced gene expression prediction and compound screening, showing improved performance over existing methods and demonstrating its application to COVID-19 drug repurposing.