Mohsen Hooshmand

RASTRO improves unitig graph path selection by using sequence-based models to identify read-supported branching points in HIV-1 and SARS-CoV-2 assemblies.

B-SMART-Former predicts drug-drug interactions between biotech and small-molecule drugs using an explainable transformer-based model, achieving high AUROC and AUPR scores.

The paper predicts synergistic drug combinations for viral diseases using attention-based models and validates two combinations against herpes simplex virus type 1.

This paper introduces BSI-Net, a graph attention network for predicting interactions between biotech and small-molecule drugs, showing improved performance over existing methods.

This paper proposes a machine/deep learning framework to predict antivirals for monkeypox using a new virus-antiviral dataset and identifies five effective drugs.