The function for much of the 3 billion letters in the human genome remain to be understood. Advances in DNA sequencing technology have generated enormous amount of data, yet we don’t have the tool to extract rules of how the genome works. Deep learning holds great potential in decoding the genome, in particular due to the digital nature of DNA sequences and the ability to handle large data sets. However, like many other applications, the interpretability of deep learning models hampers its ability to help understand the genome. We are developing deep learning architectures embedded with the principles of gene regulation and we will be leveraging billions of existing measurements of gene activity to learn a mechanistic model of gene regulation in human cells.

This is an UNPAID research project.

Faculty Advisor

  • Professor: Xuebing Wu
  • Department/School: Medicine / Systems Biology / Cancer Center / DSI
  • Location: P&S 10-401
  • We study the mechanisms of mammalian gene regulation by using integrated experimental and computational approaches. We are also exploring the therapeutic potential of CRISPR-based mRNA-targeting for treating human diseases.

Project Timeline

  • Earliest starting date: 3/1/2020
  • End date: 5/1/2020
  • Number of hours per week of research expected during Spring 2020: ~5
  • Number of hours per week of research expected during Summer 2020: ~5

Candidate requirements

  • Skill sets: Python / Deep learning / basic molecular biology / basic shell scripting
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible