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Choong Yong Ung, Ph.D., Assistant Professor

Choong Yong Ung, Ph.D., Assistant Professor ung.choongyong@mayo.edu

I am an Assistant Professor with experience in developing algorithms in the areas of bioinformatics, systems biology, and Artificial Intelligence (AI) for more than 20 years. My early research focused on developing machine learning algorithms, in particular using Support Vector Machine (SVM) to characterize pharmacological properties of drugs including herbal medicines as well as developing kinetic models to simulate cancer signaling pathways. I also devised bioinformatics approaches to characterize toxicogenomic properties of heavy metals, in particular mercury in animal models to detect environmental pollution.

Since 2013 after joining Hu Li's lab, my research interest is to uncover emergent systems mechanisms that govern genotype-phenotype interactions that shape emergent properties in biological phenotypes, including complex disease traits and drug response phenotypes. Over the past few years, I have helped developing a number of novel conceptual frameworks that enable development of innovative systems biology and AI tools in the Li'slab, in particular process-guided flow algorithm in NetDecoder [Nucleic Acids Research, 2016], "dark" cancer genes in Machine Learning Assisted Network Inference (MALANI) [Scientific Reports, 2017], and Regulostat Inferelator (RSI) [Nucleic Acids Research, 2019], PERMUTOR, the first individualized disease network algorithm [Genome Biology, 2021] and Weight Engineering, a deep learning-based algorithm to retrieve "knowledge" learned from big data by deep neural networks [Front Immunology, 2022].

Besides, I also helped to develop new systems-based perspectives for how biological systems and diseases should be perceived to better understand the underlying "manifoldness" nature of mechanisms that underpin disease etiology and drug discovery. For instance, I proposed Gene Utility Model which hypothesizes that it is how a gene is utilized in biological network dictates its importance in disease development. My recent application of GUM on neuroblastoma, the most common extracranial solid tumor in children that harbors very few somatic mutations compared to adult cancers, solved the long-sought mystery why advanced stage neuroblastomas exhibit typical segmental chromosomal aberrancies that are associated with poor prognosis [Comput Struct Biotechnol J, 2022]. In addition, I also proposed a conceptual framework called Manifold Medicine in which disease states are described by vectorial positions on several body-wide axes, including the manifoldness nature of mode-of-actions of drugs [Drug Discov Today, 2022]. More recently, I proposed Manifold Epigenetic Model (MEMo), a conceptual framework to explain how epigenetic memory arises and offer strategies that can be applied to manipulate the body-wide memory to rejuvenate aging and disease treatment [Front Cell Dev Biol, 2023].

My publications can be found here.