The central goal of our research is to design and develop effective informatics solutions for understanding complex human diseases. Partiularly we focus on integrative approaches where data mining, computational modeling and knowledge discovery are integrated 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: 

▪          Systems Biology: To advance our understanding of complex biological systems and discover novel mechanisms, we develop integrative models that utilize multi-level omics information and computational techniques in data mining and network modeling to systematically study alterations and communications of cells and organisms in response to environmental stimuli, especially the multi-facet molecular interaction associated with various developmental process.

▪          Biomedical informatics: The rapidly advancing biotechnology is generating massive high-throughput omics data on human diseases such as cancer and obesity, which has presented unprecedented opportunities as well as challenges to computational biologists to study complex human diseases from a more holistic and systems perspective. Our primary focus along this line includes the development of data-driven solutions to cancer diagnosis and treatment, mechanistic study of cancer evolution, gene regulation network modeling to decipher signaling and metabolic networks in complex diseases.

▪          Big Data, Machine Learning and Data Mining Algorithms: To facilitate the data driven research in our group and more broadly, to efficiently utilize Big Data in the bio field and address health-related challenges, we develop new statistical and computational methods for data mining and integration, pattern recognition, image processing, and knowledge discovery, with specific focus on solving problems related to automated learning, reasoning, and decision-making in biomedical research.


Funded Projects:

"AI-enabled Health Assistant System for Monitoring Focused-Activity" by NIH-funded IDeA-CTR. (06/2020-05/2021)

"Bioinformatics-guided discovery of dietary microRNA signals in obesity" supported by National Institutes of Health (08/2014-07/2017)

"A New Evolution Model for Cancer Classification and Treatment" funded by UNL Layman grant (05/01/2019-04/30/2020)

"Molecular signatures of new bioactive compounds in humans: cows milk microRNAs" supported by National Institutes of Health (08/2016-07/2021)

"Nutritive Value and Potential Health Benefits of LOL-Exosomes" funded by Purina Mills, LLC. (03/01/2019-02/28/2020)

"Prevention of human disease by food-borne microRNAs" supported by ORED UNL (08/2016-07/2018)

"Decovolution of Obesity and Cancer Links through MicroRNA Regulation”  supported by the seed grant from ORED UNL (2016-2017)

"Translating Big Data into Human Health through MicroRNA Biology” supported by ORED UNL (01/2015-2016)

"Identification of surface proteins that mediate the uptake of milk exosomes" bsupported by NPOD UNL (01/2015-2017)

"Bioinformatics workshop on Next-generation and microarray data analysis"supported by Nebraska EPSCoR (07/2014)