Gene sequencing reveals potential causative variants and gene expression patterns for further follow-up studies of gene expression and regulation mechanisms. The Cancer Dependency Map (DepMap) project uses functional genomics to pinpoint essential genes for cell growth in cancer models, aiding in new therapeutic development. DepMap identifies dependencies via CRISPR screening to find genes linked to specific cancer phenotypes. My aim for this project will be to pinpoint mutations causing cancer-driven alternative splicing and integrate this data into our model to explore its connection to cancer dependency and other genomic features, which could inform new therapeutic strategies.

Through this SuperUROP, I aim to enhance my research skills in computational biology and improve my ability to communicate findings. I am particularly interested in using machine learning to tackle complex biological problems. I look forward to applying knowledge from my computer science, biology & bioengineering courses to identify cancer dependencies that can lead to the development of more precise and effective cancer treatments.