MIT EECS — UTRC Undergraduate Research and Innovation Scholar
Computational analysis of miRNA expression data using machine learning and statistics
MicroRNAs (miRNAs) are a class of evolutionarily conserved non-coding RNAs that regulate stability and translation efficiency of target mRNAs and have a direct impact on cancer progression. Several studies show that miRNAs are aberrantly expressed in tumors and recent data strongly suggests that miRNA profiling is more robust than mRNA in tumor classification. To overcome certain limitations of existing assays, Weiss lab for Synthetic Biology used genetic circuits augmented with miRNA sensors to generate libraries of expression data from 15 target healthy and cancer cell lines. These expression data libraries, in contrast to libraries derived from microarrays and similar techniques, will be experimentally measured in real-time from large numbers of individual live cells.
I am majoring in Course 6-7, or Computational Science and Molecular Biology, which puts me right at the intersection of these two fastest growing fields of the 21st century. I am fascinated by the interdisciplinary nature of my project, and I hope the skills learned so far in MIT will help me succeed in it.