Joseph M. Taft

The paper predicts viral host range using a combination of experimental screening and deep learning to identify potential animal reservoirs for reverse zoonotic transmission.

The paper uses deep mutational learning to analyze serum antibody escape to SARS-CoV-2 variants, finding individualized escape profiles and conserved binding signatures.

This study uses synthetic coevolution to explore mutational trajectories between neutralizing antibodies and SARS-CoV-2 variants, revealing antagonistic and compensatory mutations.

This study uses deep learning to identify antibodies resistant to SARS-CoV-2 Omicron variants by predicting their binding and escape from mutations.

This paper develops deep mutational learning to predict the impact of SARS-CoV-2 spike protein mutations on ACE2 binding and antibody escape, identifying potential variants and supporting vaccine and therapeutic development.