Xiayuan Huang, PhD

Topic: A bioinformatics journey: from EHR to genetic data

Project Summary: As a postdoc at Yale Center for Biomedical Data Science, Xiayuan is going to work on high-throughput biomedical data, including electronic health records (EHRs) and genetic data. His research will focus on extending state-of-the-art machine learning approaches in health using EHRs, developing machine learning algorithms for drug discovery and adverse drug effects, and applying statistical methods to investigate the challenging problems in genetic data. Based on his PhD research, he believes family history linked EHRs succinctly encompasses shared genetic, epigenetic, and environmental features which enhance the analysis of human disease. He plans to apply machine learning algorithms in healthcare domain, such as disease risk prediction, precision medicine and clinical applications using family history linked EHRs. From the perspective of genetic data, his research work is devoted to addressing challenging problems in single-cell RNA sequencing data, developing innovative statistical models on analyzing the impact of genetic variants in human disease.