Choi Hyung Won
Associate Professor
Programme Leader, SLING
Chair, Cardiovascular-Metabolic Diseases TRP
Associate Professor, Department of Medicine
Yong Loo Lin School of Medicine
Email: mdchwc@nus.edu.sg

What are your present research interests?

Dysregulation of metabolism is the central mechanism driving a wide range of chronic human diseases. Given the growing prevalence of chronic diseases and their systemic nature, it is essential to understand the complex dynamics of a host of biochemical factors in response to prolonged exposure to chronic cell stress and maladaptive cellular responses.

Metabolomics, the analytical science for quantitative measurements of primary and secondary metabolites, as well as their dynamics and interaction with enzymes and transporters, is the key science enabling many branches of biological research. However, although technological advances in mass spectrometry have enabled us to measure parts of the global metabolome, the field is still riddled with an array of practical challenges limiting the detection coverage and quantitative reliability at an unsatisfactory level.

While some challenges are of nature related to the analytical science, especially the massive biochemical diversity of the metabolome, other key issues arise from computational bottleneck attributable to the complexity of the data produced by state-of-the-art instruments. Specifically, these issues are:

• Ambiguities embedded in chimeric/mixed fragmentation spectra due to isomers and isobars
• The lack of a comprehensive spectral database of all endogenous and exogenous compounds.

These problems do not arise as seriously in the analysis of proteins using mass spectrometry, thanks to the generation of a large number of peptide fragment signatures and the availability of in silico databases from fully sequenced genomes, respectively. These two major challenges need to be addressed in the context of small molecule analysis, with each solution complementing the other.

The future of truly metabolome- and lipidome-wide analysis depends on a synergistic coordination across three areas: (i) cutting-edge instrumentation for high-quality data production, (ii) analytical optimization for precision and throughput, and (iii) computational infrastructure and software ecosystem to maximize the value of the complex data. Advances in the GC/LC-MS instrumentation and the fundamental techniques in analytical chemistry lay the foreground for generating high-quality mass spectral data from purer background and good separation of analytes. The innovation in computational methods allows for accurate annotation of all ion signatures in the data in terms of chemical substructures and their potential functions, taking such big data from simple digital features to a knowledge base.

It is therefore crucial to pursue technological advances in all three areas together, by leveraging our existing collaboration with leading technology developers. This is where my metabolomics research interest lies.

What do you see as your future research directions?

Cheminformatics and Multi-modal Data Integration. Large-scale Statistical Computing in the era of Precision Medicine.

Does your laboratory have a particularly strong research expertise?

Computational Biology and Mass Spectrometry Informatics.

Recent Publications

1. L. Wong, H. Koh, H. Choi, …, A.M. Richards. Combined circulating microRNA and peptide biomarkers for progsnotication in heart failure. Cardiovasc. Res. 2025; cvaf065.
2. H. Koh et al., An integrated signature of extracellular matrix proteins and a diastolic function imaging parameter predicts post-MI long-term outcomes. Frontiers in Cardiovascular Medicine 2023, 10, 1123682.
3. S. Ko, G.X.L. Li, H. Choi*, J-.H. Won*. Computationally scalable regression modelling for ultrahigh-dimensional omics data with ParProx. Brief. Bioinfo. 2021, bbab256.
4. S. Ghosh, A. Datta, H. Choi. multiSLIDE: a web server for exploring connected elements of biological pathways in multi-omics data. Nat. Comms. 2021; 12(1):2279.
5. G. Teo, W. Chew, B. Burla, D. Herr, ES Tai, M. Wenk, F. Torta, H. Choi. MRMkit: automated data processing for large-scale targeted metabolomics analysis. Anal. Chem. 2020, 92 (20) 13677-13682.
6. P. Narayanaswamy, G. Teo, J. Ow, A. Lau, P. Kaldis, S. Tate, H. Choi. MetaboKit: a comprehensive data extraction tool for untargeted metabolomics. Mol. Omics 2020; 16, 436-447.