Babak Ravandi

The paper introduces two benchmark datasets for drug repurposing across rare diseases: a positive dataset from clinical trials and a negative dataset from disease ontologies, enabling evaluation of computational models for disease-drug associations.

This paper introduces a method to improve protein-protein interaction prediction by using topology-driven negative sampling and an ML pipeline that enhances generalizability and interpretability.

This paper introduces Dis2Vec, a representation learning framework for human diseases that enables therapeutic transferability prediction and disease clustering, enhancing translational research efficiency.