Title:Data Analytics and Algorithms for Biological Applications
Speaker: Zhengdao Wang, Associate Professor
Abstract: As more data become available in the biology, especially based on high throughput sequencing, there are opportunities and challenges to develop theoretical models to capture and represent the mechanisms of various biological functions, and algorithms to verify, learn and tune such models based on experimental data.
Data analysis can also help to identify possible causes for diseases and suggest possible ways of intervention and treatment. In this talk, we will first give a brief introduction and then present some of the work that has been performed on this topic, including the problem of detecting copy number variation based on genomic sequencing data and learning gene regulatory network based on perturbed time-series expression data.