Research Interests

Computational Biology, Machine Learning, Data Mining, and Statistical Methods.

Education

University of Minnesota Twin Cities, USA Mar. 2011
Ph.D., Department of Computer Science and Engineering
Thesis: Network-based Learning Algorithms for Understanding Human Disease
Inha University, KOREA Aug. 2005
M.S. (transferred), Department of Computer Science and Engineering
Advisor: Prof. Gun-Sik Jo
Inha University, KOREA Feb. 2004
B.E., Department of Computer Science and Engineering

Research Experience

Quantitative Biomedical Research Center, Department of Clinical Science, University of Texas Southwestern Medical Center, USA
Assistant Professor Jan. 2013 – Current

Masonic Cancer Center, University of Minnesota Twin Cities, USA
Principal Investigator, Research Scientist Mar. 2011 – Jan. 2013

  • Developing computational methods to integrate diverse high-throughput genomic, genetic, phenotypic, and clinical data, and custom pipelines to analyze next generation sequencing data (e.g. DNA sequence, RNA sequence, CHIP-seq, and etc) to discover underlying molecular mechanisms of cancers.

 

Genentech, Inc., USA
Research Intern Jun. 2010 – Sept. 2010
Advisor: Jinfeng Liu, Ph.D., Department of Bioinformatics and Computational Biology

  • Developed and implemented network-based integrative analysis for discovering gene modules (e.g. pathway, subnetworks or gene sets) that are commonly and cancer-type speci c dysregulated in copy number alternations across human cancers.

 

University of Minnesota Twin Cities, USA
Research Assistant Jun. 2007 – Present
Advisor: Prof. Rui Kuang, Department of Computer Science and Engineering

  • Developed and implemented network-based learning algorithms to integrate gene expression, copy number, and protein interaction networks for cancer outcome prediction and biomarker discovery.
  • Developed and implemented network-based learning algorithm to prioritize potential disease candidate genes using integrated human phenome-genome interactome networks. The algorithm can be also applicable to predict functions of genes, and new drug targets of a drug (or a combination of drugs).

 

Inha University, KOREA
Research Assistant Mar. 2004 – Jun. 2005
Advisor: Prof. Gun-sik Jo, Department of Computer Science and Engineering

  • Developed and implemented web-based tools for use in mining information on the semantic web

 

Research Grants

1. Patient Strati cation and Pathway Discovery using Genomic Data Integration (pending) Samsung Advanced Institute of Technology Role: Principal Investigator
The goal of this project is to develop novel computational tools to predict which patients who may or may not respond to drugs and pathways related with drug response to create personalized, more effective treatment strategies.
2. AR Gene Structure Alterations and Prostate Cancer Progression
American Cancer Society ($600,000), 01/01/201212/31/2015.
PI: Scott Dehm
The goal of this project is to study cell- and xenograft-based models of prostate cancer progression to understand the mechanisms by which alternatively-spliced, truncated AR isoforms are synthesized, translocate to the nucleus, bind DNA, activate transcription, and mediate resistance to AR-targeted therapies in CRPCa.
3. Genetic Background and the Angiogenic Phenotype in Cancer
AKC Canine Health Foundation ($230,424), 01/01/201012/31/2012.
PI: Jaime Modiano
The major goals of this project are to con rm that heritable traits (breed) have distinct and discrete in uence on gene expression signatures of canine hemangiosarcoma, and that these traits lead these tumors to respond directly to angiogenic and pro-inflammatory signals.
4. Genomic Signatures of Colorectal Cancer
Masonic Cancer Center ($180,000) 03/01/201202/28/2014.
PI: David Lagaespada
This proposal enlists a multi center team at the University of Minnesota and the VUMC in Amsterdam to identify markers of colorectal cancer based on somatic DNA mutations and speci c chromosomal alterations for use in diagnosis and therapy.
5. The role of AR gene rearrangements in prostate cancer progression
R01 NIH
Role: Co Investigator
This project is designed to study AR gene rearrangements in prostate cancer progression using next-generation sequencing analysis.
6. Ecient Algorithms and Database Architecture for Big Bio Data (pending)
The Small and Medium Business Administration (Korea Government Agency) ($400,000)
Role: Co Investigator
This project is designed to develop algorithms and design infrastructures for the discovery of biomarkers for clinical use from Big Bio data.
7. Comparative Assessment of the Etiology and Clonal Diversity of Non-Hodgkin Lymphoma (sub-
mitted)
Mn Partnership for Biotechnology and Medical Genomics
PI: Jaime Modiano
This project is designed to develop and optimize infrastructure for comparative multispecies systems approaches to study non-hodgkin lymphoma.
8. Tumor-Microenvironment Interactions in Osteosarcoma Progression (submitted)
Department of Defense
PI: Jaime Modiano
This project is designed to understand how bidirectional interactions between osteosarcoma and the host tumor microenvironment contribute to aggressive biological behavior and metastasis in this pediatric tumor.

Peer-reviewed Publications
1.Yingming Li, Siu Chiu Chan, Lucas Brand, TaeHyun Hwang, Kevin Silverstein and Scott Dehm,\Androgen receptor splice variants mediate enzalutamide resistance in castration-resistant prostate cancer cell lines”,Cancer Research November 2012, Impact factor: 7.856
2.TaeHyun Hwang, Maoqiang Xie, Gowtham Atluri, Sanjoy Dey, Vipin Kumar, Changjin Hong and Rui Kuang.,\Co-clustering Phenome-genome for Phenotype Classi cation and Disease Gene Discovery”, Nucleic Acids Research June 2012; doi: 10.1093/nar/gks615, Impact factor: 8.026
3.Yingming Li*, TaeHyun Hwang*, LeAnn Oseth, Betsy Hirsch, Robert Vessella, Kenny Beckman, Kevin Silverstein, and Scott Dehm,\AR intragenic deletions linked to androgen receptor splice variant expression and activity in models of prostate cancer progression”, Oncogene, Jan. 2012; doi:10.1038/onc.2011.637 Impact factor: 7.414(*Joint rst authors)
4.Young-Mi Kim, Matthew Stone, Tae Hyun Hwang, Yeon-Gil Kim, Timothy J. Grin, and Do-Hyung Kim, \SH3BP4 is a negative regulator of amino acid