Biography
Dr. Ge's teaching and research focus on bioinformatics, artificial intelligence (AI) and data science. Bioinformatics is a discipline that combines statistics, computer science and biological context to analyze large genomics datasets.
Education
- Ph.D. in engineering | University of Tokyo, Japan | 2000
- M.S. in condensed matter physics | Beijing University of Science and Technology, China | 1997
- B.S. in applied physics | Beijing University of Science and Technology, China | 1994
Academic and Professional Experience
Academic Interests
Academic Interests
- Data science
- AI
- Bioinformatics
- Genomics
Academic Responsibilities
- STAT 435/535 Applied Bioinformatics, spring semesters for students with biological background.
- STAT 736 Bioinformatics, fall semester for students in statistics, computer science departments or others with programming skills.
- STAT 414/514 Basic R programming, spring semester, one credit online course.
- STAT 492 Data visualization using R.
- STAT 442 Exploratory and Cloud-based Data Analysis. A project-based course where students analyze real datasets and present and discuss every week.
Research and Scholarly Work
Areas of Research
- Development of tools (iDep and ShinyGo) and resources (pathway databases) for the interpretation of large genomic data.
- Using AI, especially Large Language Models like ChatGPT, to help analyze and visualize data.
Grants
- An interactive tool for in-depth and reproducible analysis of RNA-seq data. National Institutes of Health, SDSU, Sept. 2020, $869,378.
- The Immune regulation of macrophage antibody dependent cellular phagocytosis. National Institutes of Health, SDSU, July 2020, $1,500,000.
- BioSystems Networks and Translational Research - Insights into Inflammation (BioSNTR-II). National Institutes of Health, March 2022, $12,000,000.
- Adventures in Drug Discovery: Integrating Data Science into the Science Curriculum, National Institutes of Health, July 2023, $1,000,000.
- The Biochemical Spatiotemporal NeTwork Resource (BioSNTR): Building a bio-Economy in South Dakota. National Science Foundation and the State of South Dakota, July 2013, $25,000,000.
- Large-scale expression analysis of natural antisense transcripts, National Institutes of Health, SDSU, April 2009, $835,586.
Publications
- Ge X., D. Jung, ShinyGO: a graphical enrichment tool for animals and plants, Bioinformatics, 36:2628-2629, 2020.
- Ge S.X., Son E.W., Yao R.N.: iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data, BMC Bioinformatics 19:124, 2018.
- Ge S.X. Exploratory bioinformatics investigation reveals importance of "junk" DNA in early embryo development. BMC Genomics. 18(1):200, 2017.
- Ling M.H., Ban Y., Wen H., Wang S.M., Ge S.X. Conserved expression of natural antisense transcripts in mammals. BMC Genomics. 14:243, 2013.
- Shi L., et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nature Biotechnology, 28(8):827-38, 2010.
- Ge X., Iwata S. Learning the parts of objects by auto-association. Neural Network.15(2):285-95, 2002.
Associated Areas
