이현주(Hyunju Lee)

학력

  • 2006 University of Southern California (Ph.D. – Computer Science)
  • 1999 Seoul National University (M.S. – Computer Engineering)
  • 1997 KAIST (B.S. – Computer Science)

경력

  • 2007~Present Gwangju Institute of Science and Technology, Professor
  • 2006 ~ 2007 Brigham and Women’s Hospital and Harvard Medical School, Postdoctoral Fellow
  • 2002 ~ 2006 University of Southern California, Research Assistant
  • 2001 ~ 2002 Korea Wisenut Inc., Senior Engineer
  • 1998 ~ 2001 Intus Technology inc., Xinics Inc., Engineer

연구분야

  • Computational biology
  • Integration of multiple biological datasets for predicting protein functions
  • Integration of DNA copy number changes and gene expressions in cancer
  • Visualization of biological datasets
  • Data mining in network structure
  • Machine learning, semantic web, etc.

연구실소개

  • Data Mining & Computational Biology LaboratoryThe Data Mining & Computational Biology Laboratory is focusing on developing novel data mining methods for diverse area from internet to life science. Our goal is to provide intuitive and well organized information by mining heterogeneous resources. Research efforts also include addressing key questions in biology and medicine.-Integration of multiple biological datasets
    Advance of high-throughput technology in biology have generated large-scale data sets at the level of DNA, RNA, and proteins. To enhance understanding of cell activities, it is necessary to develop methods to integrate diverse datasets. The network structure of proteins in the cell provides evidences for functions of proteins as proteins perform their functions by collaborating with others. We use protein interactions, domain (a subunit of a protein) interactions, protein localizations, and gene expressions to identify protein functions.-Integration of DNA copy number changes and gene expressions in cancer
    Decades of research have discovered that cancer is caused by cumulated changes in DNA structure and transcriptional changes. Among various DNA structural changes, copy number aberrations (CNAs) represent both amplifications and deletions of chromosomes ranging from 0.5 to 10 Mb in size. CNAs of oncogenes and tumor suppressor genes have been reported as causatively related with initiation, development and progression of cancer. While CNAs are structural changes, measuring the level of gene expressions provides additional information about whether those changes result in functional changes. We develop algorithms to combine CNAs and gene expression datasets to identify new biomarkers in cancer.-Visualization of biological datasets
    Visualization of multiple types of high-throughput data such as gene expression, copy number change, epigenetic modification, and DNA-protein binding is a critical for interpreting and generating novel hypothesis. These experimental data should be combined with the annotations in current version of genome database such as UCSC Bioinformatics Database, NCBI, and Ensemble database. Mapping different types of data with a unified scheme is getting more important. We develop software to simultaneously map and visualize the different types of data.
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