About Me

I am Wei Zhang, an assistant professor at the Department of Computer Science and a core member for the Genomics and Bioinformatics Cluster at University of Central Florida. My primary research interest is computational biology, an interdisciplinary research area where computational, mathematical and statistical methods are applied to solve biology problems. My research covers several important topics in cancer transcriptome, spanning from technique-driven research that aims at developing graph-based learning algorithms for cancer transcriptome analysis with prior knowledge, to hypothesis-driven investigation of specific biological problems.

I received my Ph.D and M.S. from the Computer Science and Engineering Department at University of Minnesota-Twin Cities. My advisor are Dr. Rui Kuang and Dr. Baolin Wu. I did my Bachelor thesis in Computer Science from Winona State University under Dr. Mingrui Zhang's supervision. Before joining UCF in 2017, I was a research associate at University of Minnesota for two years.

Zhang-Lab Homepage

Research Interests

  • Computational Biology and Bioinformatics
  • Machine Learning
  • Biostatistics
  • Teaching

  • Fall 2020: COP5537 Network Optimization
  • Spring 2020: COT3100H Introduction to Discrete Structure
  • Fall 2019: COP5537 Network Optimization
  • Spring 2019: COT3100H Introduction to Discrete Structure
  • Fall 2018: COP5537 Network Optimization
  • Spring 2018: COT3100H Introduction to Discrete Structure
  • Fall 2017: COP5537 Network Optimization
  • Publications

    Selected publications (Show all publications) ORCID Google Scholar

  • Wei Zhang, Raphael Petegrosso, Jae-Woong Chang, Jiao Sun, Jeongsik Yong, Jeremy Chien, and Rui Kuang. A Large-Scale Comparative Study of Isoform Expressions Measured on Four Platforms. BMC Genomics, 2020. (In Press)

  • Jiao Sun, Jae-Woong Chang, Teng Zhang, Jeongsik Yong, Rui Kuang, and Wei Zhang#. Platform-integrated mRNA Isoform Quantification. Bioinformatics, 2019. (#Corresponding author) doi:10.1093/bioinformatics/btz932[Download]

  • Zhibo Wang, Zhezhi He, Milan Shah, Teng Zhang, Deliang Fan, and Wei Zhang#. Network-based Multi-Task Learning Models for Biomarker Selection and Cancer Outcome Prediction. Bioinformatics, 2019. (#Corresponding author) doi:10.1093/bioinformatics/btz809[Download]

  • Jae-Woong Chang, Hsin-Sung Yeh, Meeyeon Park, Luke Erber, Jiao Sun, Sze Cheng, Alexander M. Bui, Naima Ahmed Fahmi, Ryan Nasti, Rui Kuang, Yue Chen, Wei Zhang#, and Jeongsik Yong#. mTOR-regulated U2af1 tandem exon splicing specifies transcriptome features for translational control. Nucleic Acids Research, 2019. (#co-corresponding authors) doi:10.1093/nar/gkz761

  • Jae-Woong Chang*, Wei Zhang*, Hsin-Sung Yeh, Meeyeon Park, Chengguo Yao, Yongsheng Shi, Rui Kuang#, and Jeongsik Yong#. An Integrative Model for Alternative Polyadenylation, IntMAP, Delineates mTOR-modulated Endoplasmic Reticulum Stress Response. Nucleic Acids Research, 2018. (*Joint first authors) doi:10.1093/nar/gky340 [Download]

  • Wei Zhang, Jeremy Chien, Jeongsik Yong, and Rui Kuang. Network-based Machine Learning and Graph Theory Algorithms for Precision Oncology. npj Precision Oncology, 2017. doi:10.1038/s41698-017-0029-7

  • Wei Zhang, Jae-Woong Chang, Lilong Lin, Kay Minn, Baolin Wu, Jeremy Chien, Jeongsik Yong, Hui Zheng, and Rui Kuang. Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis. PLoS Comput Biol, 2015. doi:10.1371/journal.pcbi.1004465 [Download]

  • Jae-Woong Chang*, Wei Zhang*, Hsin-Sung Yeh, Ebbing de Jong, Semo Jun, Kwan-Hyun Kim, Sun Sik Bae, Kenneth Beckman, TaeHyun Hwang, Kye-Seong Kim, Do-Hyung Kim, Rui Kuang, and Jeongsik Yong. mRNA 3'UTR Shortening is a Molecular Signature of mTORC1 Activation. Nature Communications, 2015. doi:10.1038/ncomms8218 (*Joint first authors)

  • Wei Zhang, Takayo Ota, Viji Shridhar, Jeremy R Chien, Baolin Wu, and Rui Kuang. Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment. PLoS Comput Biol, 2013. doi:10.1371/journal.pcbi.1002975 [Download]