My group develops computational approaches to reconstruct signaling pathways and transcriptional regulatory networks from multiple types of high-throughput data using techniques from machine learning and graph theory. We emphasize dynamic networks and disease applications in virology and oncology.
Ph.D., 2012, Carnegie Mellon University
- Network inference with Granger causality ensembles on single-cell transcriptomic data. Atul Deshpande, Li-Fang Chu, Ron Stewart, Anthony Gitter. bioRxiv, 2019. doi:10.1101/534834
- Practical model selection for prospective virtual screening. Shengchao Liu*, Moayad Alnammi*, Spencer S Ericksen, Andrew F Voter, Gene E Ananiev, James L Keck, F Michael Hoffmann, Scott A Wildman, Anthony Gitter. Journal of Chemical Information and Modeling. 59:1, 2019.
- Synthesizing signaling pathways from temporal phosphoproteomic data. Ali S Köksal, Kirsten Beck, Dylan R Cronin, Aaron McKenna, Nathan D Camp, Saurabh Srivastava, Matthew E MacGilvray, Rastislav Bodík, Alejandro Wolf-Yadlin, Ernest Fraenkel, Jasmin Fisher, Anthony Gitter. Cell Reports. 24:13, 2018.
- ‘Protein Pinball’ machine illuminates intricacies of bioinformatics research
- Scholarly snowball: Deep learning paper generates big online collaboration
- You may also like … Algorithms that improve drug discovery
- CAREER award to explore dynamics of biology
- A pathway for understanding cancer’s origin
- Anthony Gitter: Taking the statistical road less traveled