Date: 2021-01-15

Time: 15:30-16:30 (Montreal time)

Zoom Link

Meeting ID: 843 0865 5572

Passcode: 690084

Abstract:

During the last decade, hundreds of machine learning methods have been developed for disease outcome prediction based on high-throughput genomics data. However, the quality of the input genomics features and the output clinical variables has been ignored in these algorithms. In this talk, I will introduce two studies that develop methods to learn more accurate molecular signatures and drug response values for cancer research. These studies are supported by NSF, NIH, and Moffitt Cancer Center.

Speaker

Dr. Wei Zhang is an Assistant Professor in the Department of Computer Science and Genomics and Bioinformatics Cluster at UCF. His research interests include computational biology and machine learning. His research has centered on investigating the role of transcriptome variants in diseases, spanning from technique-driven research (e.g., algorithm development for disease outcome prediction), to hypothesis-driven investigation of specific biological problems. Dr. Zhang received NSF CRII award in 2018 and NIDDK dkNET New Investigator Award in 2020. Lab webpage: https://server.cs.ucf.edu/compbio/