Research

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The central goal of our research is to understand complex biological systems, such as cells, tissues, or even human diseases such as cancer and obesity, through data integration, computational modeling and knowledge discovery, to systematically understand the alterations of cells and organisms in response to environmental stimuli, and to elucidate the molecular interaction network involved in complex biological processes. Our principle research interests lie in the following areas: 

▪          Computational Systems Biology: all components in a cell work together to perform specific functions and all cells work together to sustain life. Elucidation of underlying molecular mechanisms of a complex biological process requires understanding the interplay between different cells and different types of molecules such as genes, proteins, metabolites, non-coding small RNA, and others. We develop computational methods to integrate biological data at multiple molecular levels to advance the understanding of biological interaction networks in complex systems.

▪          Biomedical informaticsRapidly advancing biotechnology is generating large-scale high-throughput genomic and other types of omic data on human diseases such as cancer and obesity. Such massive data has presented unprecedented opportunities as well as challenges to computational biologists to study human diseases from fundamentally novel perspectives, allowing us to examine complex diseases in a more holistic and systems view. Our primary research in this line includes developments of data mining framework for biomarker discovery, cancer genome evolution model and next-generation sequencing data analytical pipeline, and network modeling to decipher metabolic network in human diseases in response to various environmental changes.

▪          Machine Learning and Data Mining Algorithms: To efficiently translate Big Data into biomedical resarch and address complex biological problems as well as data science challenges, we develop statistical and computational methods for data integration, pattern recognition, and knowledge discovery and particularly focus on solving problems related to automated learning, reasoning and decision-making.

Publications