Bio-Data Science and Education Group

The Bio-Data Science laboratory is focused on the development of statistical approaches for analysing and resolving platform-specific idiosyncrasies in multi-omics data; identifying and resolving confounding issues such as batch effects, technical bias and missing values in high-dimensional data; and developing robust biomarker and drug target prediction techniques using a combination of machine learning and enhanced in silico validation techniques. This lab is also interested in Bio-education, with an emphasis on the use of new AI-based technologies, text-mining and high-impact pedagogical practices (experiential learning), to enhance the quality of biological and biotechnological education. ​


Wilson GohLead​ PI
Wilson ​Goh
Assistant Professor

Phone: (65) 6904 7149
Office: SBS-03N-28

Allen Chong
Senior Research Fellow

Xue Haitao
Research Associate

Nah Hui Kang, Christopher
Research Assistant

Phua Ser Xian
Research Assistant



  1. Design and development of digital coaching environment for Deeper Experiential Engagement Projects: coevolution of education with Artificial Intelligence​
  2. Engaging students as partners in the context of a scientific-thinking course with OBTL elements​
  3. Identify Essential Genes to Construct A Functional Unfolded Protein Response Programme Using Synthetic Biology​
  4. Evaluating the Clinical Utility of Immune Phenotypes in SchizophreniatIdes
Full list of publications can be found here. [1] [2]
  • Goh WWB*, Wong LS. Turning straw into gold: how to build robustness into gene signature inference. Drug Discovery Today, 24(1):31-36, Jan 2019
  • Goh WWB*, Sze CC. AI paradigms for teaching biotechnology. Trends in Biotechnology, 37(1): 1-5, Jan 2019
  • Goh WWB*, Wong LS. Advanced bioinformatics methods for practical applications of proteomics. Briefings in Bioinformatics, 20(1):347-355, Jan 2019​
  • Goh WWB*, Wong LS. Breast Cancer Signatures Are No Better Than Random Signatures Explained. Drug Discovery Today, 23(11):1818-1823, Nov 2018​
  • Goh WWB*, Wong LS. Dealing with confounders in -omics analysis. Trends in Biotechnology, 36(5):488-498, May 2018​
  • Goh WWB*, Wong LS. Integrating networks and proteomics: moving forward. Trends in Biotechnology, 34(12):951-959, Dec 2017​
  • Goh WWB*, Sng J, Yee JY, See YM, Lee TS, Wong LS, Lee J. Can peripheral blood-derived gene expressions genetically characterize high risk subjects for psychosis? Computational Psychiatry, 0(1):1-16, Oct 2017​
  • Goh WWB*, Wong LS. NetProt: Complex-based feature selection. Journal of Proteome Research, 16(8):3102–3112, June 2017
  • Goh WWB*, Wang W, Wong LS. Why batch effects matter in omics data, and how to avoid them. Trends in Biotechnology, S0167-7799(17)30036-7, Mar 2017
  • Goh WWB*, Wong LS. Advancing clinical proteomics via networks: A tale of five paradigms. Journal of Proteome Research, 15(9):3167-3179, Jul 2016​