- Research Program Mentor
MD candidate at Albert Einstein College of Medicine
Case reports in medicine, computational protein design, cryo-EM image analysis, computational genomics, telemedicine, AI in medicine, biochemistry, web development, data journalism
BioHi, my name is Christine and I'm so grateful for the opportunity to mentor students and give back to the community. I am a 2021 graduate of Duke University and M3 at Albert Einstein College of Medicine. My main academic interest is in the intersection of medicine and computer science, and some of the ways I've explored this is through my web development work with the health tech startup Cydoc, research in developing a neural network to predict COVID-19 in patients using gene expression, and coursework in computational protein design. In the future, I hope to pursue a surgical subspecialty or interventional radiology due to my passion for anatomy, dissections and hands-on work. In my free time, I enjoy playing sports (such as tennis, squash, volleyball) and running.
Binding site analysis of potential inhibitors of SARS-CoV-2 S2
Therapeutics are needed to control the spread of SARS-CoV-2 and the S2 region is an effective target due to its highly conserved nature. In this project, we will find examples of existing inhibitors targeting this region in similar viruses (i.e. SARS-CoV, MERS-CoV) and dock them to SARS-CoV-2 S2 to understand the binding site interactions. Then, we will re-design these inhibitors to best fit SARS-CoV-2 S2 and improve upon our binding interactions in the final project.
Application of cryoDRGN for cryo-EM image analysis
Cryo-EM is a revolutionary imaging technique to capture the molecule representation of proteins, though AI is required to denoise and reconstruct the 2D projections of these proteins. CryoDRGN is a recent paper demonstrating improved ability to reconstruct proteins through neural networks, so in this project we will validate the results of cryoDRGN by applying it to another dataset from the EMPIRE database as well as learn the details of neural networks and cryo-EM image analysis.
Gene network analysis in human disease
Gene networks are promising methods to predict connections between genes and human disease. In this project, we will use gene expression and biology pathways (from Enrichr) to develop a neural network to predict disease (i.e. COVID-19).
Literature review in medicine
The human body normally follows the same design, but occasionally we run into anatomical variations. For instance, having a short indicis propius is common in up to 10% of patients, which can lead to negative consequences if patients require tendon transfer for another issue, or need to have hand surgery. Other anatomic variants of consequence include those of the gallbladder ducts, which can be of consequence for gallbladder surgeries. These anatomic variants can make the difference between successful and failed surgeries, so it is important to review these variants and increase awareness. Thus, a possible project is having a literature review of observed variants and their possible surgical consequences. These are relatively simpler projects that are easily publishable.
Detecting trends in surgical and medical patients
Through chart review, we can collect data on patient outcomes based on their medical and surgical management and detect trends to improve the process of patient care. For example, can we use statistics and AI to determine the difference between stroke patient outcomes by hospital? These are highly publishable projects with promising outcomes.