Dimple Amitha Garuadapuri Rose Hills
Predicting Secondary and Tertiary RNA Structure via Machine Learning
RNA molecules are known to play critical roles in various cellular processes, and understanding their structure provides a lot of insight into their function and the mechanisms they use. Recent advancement with AlphaFold and other protein prediction algorithms have proven the potential of utilizing machine learning techniques to computational predict structure. Solving the problem of protein structure is not only significantly enhancing our understanding, but also streamlining the drug delivery and therapeutic development process. Being able to computationally predict accurate RNA structures would similarly enhance our understanding of noncoding RNA function, and lead to the development of better therapeutics and drug delivery systems. This project aims to curate better quality RNA structure datasets and build upon existing machine learning models to accurately predict secondary structure and tertiary structure of RNA molecules.
Message To Sponsor
I am very grateful to the Rose Hills foundation for supporting students through this fellowship. Thank you so much for providing this opportunity to be able to explore a scientific niche that interests me while expanding my skills in conducting research, interacting with a community of like-minded individuals, and being supported financially to do so relatively worry-free. I look forward to learning so much and continuing my educational career this summer through research!