Project: Using Machine Learning to Predict the Health of HIV-Infected Patients
Duration: Summer 2012 – Spring 2014
Funding: Bucknell University PUR, Biology Dept. and CS Dept. Funding
ABSTRACT
HIV is one of the most devastating viruses to hit mankind in modern history. About half of people infected will acquire AIDS. For some, however, the virus will lay in a stage known as “clinical latency” for 10, perhaps up to 20 years; in this stage, the symptoms are mild, sometimes even non-existant. This study aims to investigate the potential existance of specific patterns in the genome of HIV, and the prognosis of the infected patient. Discovery of such patterns could help aid researchers in improved understanding of the genetics of HIV, assisting in identifying potential patterns that researchers should look for to help infected doctors predict patient prognosis more accurately. Moreover, the identification of specific mutations or recurring patterns that are highly deleterious to the infected patient could aid in the development of drugs to target those genes containing the deleterious mutations.
ACHIEVEMENTSS
- Honors thesis defense passed – April 25, 2014
- Short paper and poster: ACB BCB ’13 – ACM International Conference on Bioinformatics, Computational Biology and Biomedicine, Sept 22-25, Washington DC
- Oral presentation: Third Annual Susquehanna Valley Undergraduate Research Symposium, SVURS 2013, August 6, Geisinger Research, Danville, PA
- Winner for oral presentation – One of three chosen out of 67 submissions!
- Poster: Kalman Research Symposium 2013, April 13, Bucknell University, Lewisburg, PA.
POST GRADUATION UPDATES
Charles was accepted into to a pre-med program at Temple University, and will be starting medical school immediately thereafter.