Education

B.S.
Soochow University
2004

M.S.
Gyeongsang National University
2006

Ph.D.
Clemson University
2011

Postdoctoral Fellow
Cold Spring Harbor Laboratory
2011-2015

Expertise

Bioinformatics

Mental Disorders

Next-Generation Sequencing

Machine Learning

Protein Structure Modeling

Academics

Bioinformatics (BIOL/BIOG 419)

Biostatistics (BIOL/BIOG 430)

Research

Specialty

Bioinformatics

Funding

• NSF Excellence in Research: Analysis of Rare Mutations and Post-translational Modifications in Peroxidases (PI)
• NSF Harnessing the Data Revolution (HDR): Data Science Corps (DSC) : Collaborative Research: ADACE: Anthropocentric Data Analytics for Community Enrichment (Co-PI)
• NSF Targeted Infusion Project: Inspiring, engaging and retaining underrepresented students in computing research and the emerging field of data science (Co-PI)        
• DoD Center of Excellence in Artificial Intelligence & Machine Learning (CoE-AIML). (Senior Personal)
• Howard University RCMI Investigator Development Core (IDC) Research Grants. (PI)
• AIM-AHEAD) Data Science Training Core (DSTC) Faculty Teaching Buyout and Curriculum Development Pilot Program (PI)
• NIH Howard University summer research education experience program in oral health. (Research Mentor) 

Group Information

Bioinformatics is a new scientific discipline that utilizes computational approaches for understanding the biological meanings behind the large amounts of complex data generated from high-throughput technologies. The goal of the Teng lab is to develop and apply bioinformatics approaches for analyzing the genetic variations associated with human diseases and discovering biological knowledge hidden in the massive data sets. We are currently focused on three research areas including next-generation sequencing, machine learning and protein structure modeling. 

NEXT-GENERATION SEQUENCING (NGS) technologies make the sequencing much faster and cheaper, which have revolutionized genetic research and accelerated precision medicine. We are using whole genome sequencing and targeted sequencing to identify the sequence variants that contribute to major psychiatric disorders including schizophrenia, bipolar disorder and major depressive disorder. We are applying RNA sequencing to study gene expression changes in model organisms. In addition, we are developing bioinformatics tools and databases to analyze NGS data for mental illness and cancer research. 

MACHINE LEARNING can recognize complex and hidden pattern from massive data sets, and it is particularly appealing for biological knowledge discovery in big data. We are developing new machine learning methods to analyze NGS data, disease-causing mutations and protein post-translational modifications.

PROTEIN STRUCTURE MODELING provides an efficient and accurate way to investigate the functional effects of coding sequence variants by mapping them on the corresponding structure models. We are using homology modeling and energy calculation approaches to estimate the effects of missense variants on protein stability and protein-protein interactions, and investigate the roles of the mutations in complex diseases such as obesity, cancers and psychiatric disorders.