I lead research projects at the intersection of machine learning and experimental biology.
Most recently, I led a combined computational/experimental team developing a novel approach to RNA editing guide design at Deep Genomics. Before that, I developed predictive models of transcriptional regulation at CAMP4 Therapeutics; and built the computational biology team at Kintai Therapeutics (now part of Sail), where I was also responsible for establishing the company’s machine learning strategy.
In my academic work, I developed computational tools to help understand microbial communities and their impact on human health as a postdoc in the lab of Georg Gerber at Brigham and Women’s Hospital, and nonlinear genome-scale optimization models of metabolism in C4 plants during my PhD thesis research with Chris Myers at Cornell University.
Bogart, E. , R. Creswell, and G. K. Gerber (2019). MITRE: inferring features from microbiota time-series data linked to host status. Genome Biology 20:186.
Bucci, V., B. Tzen, N. Li, M. Simmons, T. Tanoue, E. Bogart, L. Deng, V. Yeliseyev, M. L. Delaney, Q. Liu, B. Olle, R. R. Stein, K. Honda, L. Bry, and G. K. Gerber (2016). MDSINE: Microbial dynamical systems inference engine for microbiome time-series analyses. Genome Biology 17(1):121.
Allegretti, J. R., S. Kearny, N. Li, E. Bogart, K. Bullock, G. K. Gerber, L. Bry, C. B. Clish, E. Alm, and J. R. Korzenik (2016). Recurrent Clostridium difficile infection associates with distinct bile acid and microbiome profiles. Alimentary Pharmacology and Therapeutics 43(11):1142-53.
Bogart, E. and C. R. Myers (2016). Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves. PLoS ONE 11(3): e0151722.
Franck, C., W. Ip, A. Bae, N. Franck, E. Bogart, and T. T. Le (2008). Contact-mediated cell-assisted cell proliferation in a model eukaryotic single-cell organism: an explanation for the lag phase in shaken cell culture. Phys. Rev. E 77: 041905