Our computational research spans many biomedical application areas. One common theme is using biological networks to connect diverse data and provide a cohesive view of a cellular process. We develop new computational methods and work with collaborators to apply them to study specific conditions and diseases, especially viral infection and cancer. We also use machine learning to guide high-throughput biological experiments for drug discovery and protein engineering.
Ph.D., 2012, Carnegie Mellon University
- Network inference with Granger causality ensembles on single-cell transcriptomics. Atul Deshpande, Li-Fang Chu, Ron Stewart, Anthony Gitter. Cell Reports. 38:6, 2022.
- Neural networks to learn protein sequence–function relationships from deep mutational scanning data. Sam Gelman, Sarah A Fahlberg, Pete Heinzelman, Philip A Romero, Anthony Gitter. Proceedings of the National Academy of Sciences of the United States of America. 118:48, 2021.
- Evaluating scalable supervised learning for synthesize-on-demand chemical libraries. Moayad Alnammi, Shengchao Liu, Spencer S Ericksen, Gene E Ananiev, Andrew F Voter, Song Guo, James L Keck, F Michael Hoffmann, Scott A Wildman, Anthony Gitter. ChemRxiv, 2021. doi:10.26434/chemrxiv-2021-fg8z9