Dr. Gen Li is an Assistant Professor in the Department of Biostatistics. He is devoted to developing new statistical methods for analyzing complex biomedical data, including multi-way tensor array data, multi-view data, and compositional data. His methodological research interests include dimension reduction, predictive modeling, association analysis, and functional data analysis. He also has research interests in scientific domains including microbiome and genomics.
Alzheimer’s disease (AD) afflicts more than 5 million people in the United States and is gaining widespread attention. Over 400 clinical trials were run between 2002 and 2012, but only one trial has resulted in a marketable product. One of the most common explanations for these failures is likely the consideration of Alzheimer’s as a homogeneous disease. In many cases, individuals within the same group respond to a drug in different ways. Given the highly complex nature of AD, the likelihood of identifying a single drug to provide meaningful benefits to every patient is minimal. There is a pressing and unmet need to develop personalized treatment plans based on each patients’ omics profiles.
To solve this problem, my research focus is to develop a data-driven computational approach to predict drug responses for individuals with AD. This approach is based on the patients’ metabolomics and transcriptomics profile and publicly available drug databases. Transcriptomics and metabolomics are increasingly being used to corroborate our interpretation of the pathophysiological pathways underlying AD. Integration of metabolomics and transcriptomics will guide the development of precision medicine for AD. In particular, I used the metabolome and transcriptome profiles of Alzheimer’s patients from ADNI database. For each patient, I identify his/her dysregulated pathways from their metabolome profiles and his/her specific gene regulatory network from their transcriptome profiles. My preliminary data suggested that each patient with Alzheimer’s has distinct dysregulated pathways and gene regulatory network. Drug selection based on a patient’s specific metabolome and transcriptome profiles offers a tremendous opportunity for more targeted and effective disease treatment and it represents a critical innovation towards personalized medicine for AD. My long-term goal is to become an independent investigator in computational biology with a focus on translating omics data to bedside application. The overall objective of my research is to combine metabolomics and gene expression data with drug data using advanced machine learning algorithms to personalize medicine for AD.