PILOT SEMINAR: Yunan Luo, "Machine learning for large-and small-data biomedical discovery"
- Event Type
- Seminar/Symposium
- Sponsor
- The Department of Computer Science
- Virtual
- Join online
- Date
- Feb 23, 2021 11:00 am - 12:30 pm
- Contact
- Jancie Harris
- jlphili2@Illinois.edu
- Views
- 207
- Originating Calendar
- Siebel School Speakers Calendar
Abstract:
In modern biomedicine, the role of computation becomes more crucial in light of the ever-increasing growth of biological data, which requires effective computational methods to integrate them in a meaningful way and unveil previously undiscovered biological insights. In this talk, I will discuss my research on machine learning for large- and small-data biomedical discovery. First, I will describe a representation learning algorithm for the integration of large-scale heterogeneous data to disentangle out non-redundant information from noises and to represent them in a way amenable to comprehensive analyses; this algorithm has enabled several successful applications in drug repurposing. Next, I will present a deep learning model that utilizes evolutionary data and unlabeled data to guide protein engineering in a small-data scenario; the model has been integrated into lab workflows and enabled the engineering of new protein variants with enhanced properties. I will conclude my talk with future directions of using data science methods to assist biological design and to support decision making in biomedicine.
Bio:
Yunan Luo (http://yunan.cs.illinois.edu/) is a Ph.D. student advised by Prof. Jian Peng in the Department of Computer Science, University of Illinois at Urbana-Champaign. Previously, he received his Bachelor’s degree in Computer Science from Tsinghua University in 2016. His research interests are in computational biology and machine learning. His research has been recognized by a Baidu Ph.D. Fellowship and a CompGen Ph.D. Fellowship.