Machine Learning for Large and Small Data Biomedical Discovery
Yunan Luo, University of Illinois at Urbana-Champaign
3:55 p.m.–4:55 p.m., March 25, 2021 | Zoom
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, Yunan Luo of the University of Illinois at Urbana-Champaign will discuss his research on machine learning for large and small data biomedical discovery.
First, he 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, Luo 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. He will conclude the talk with future directions of using data science methods to assist drug development and biological design.
Yunan Luo is a Ph.D. student advised by Prof. Jian Peng in the Department of Computer Science at the University of Illinois at Urbana-Champaign. He earned 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.
Contact Ginny Watterson for Zoom link.