CMEB Group

Research

OUR INTEREST

Investigating the complex network of multifaceted diseases with major global burden by integrating variants in multiple omic levels, biomarkers and environmental data collected in diverse populations, which may deliver a novel preventive approach, diagnoses, and treatment.

The Big Picture

Different genomic methodologies have been applied to examine the complex genetic architecture of metabolic diseases such as diabetes, cardiovascular diseases (CVD), and cancer. Increasing integration of cutting edge genomic science into population-based epidemiologic investigation has been not only accelerating and ameliorating the understanding of the genetic susceptibility of these complex traits, but also utilizing the identified genes for disease intervention and risk prediction. Here illustrates what we have been doing: 

Research Area

  • 1. Diseases Determinant Identification
    Related publications:
    - L. Shu*, K. K. Chan*,et al. Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States. PLoS Genet(2017). *L. Shu and K.K. Chan contributed equally to the manuscript
    - J. Liu, et al. Mendelian randomization-based exploration of red blood cell distribution width and mean corpuscular volume with risk of hemorrhagic strokes. Human Genetics and Genomics Advances (2022). - He, Qian, et al. Non-steroidal anti-inflammatory drug target gene associations with major depressive disorders: a Mendelian randomisation study integrating GWAS, eQTL and mQTL Data. The Pharmacogenomics Journal (2023).
  • 2. Predictive Modelling
    Related publications:
    - R. X. Huang, et al. Stroke Mortality Prediction Based on Ensemble Learning and the Combination of Structured and Textual Data. Computers in Biology and Medicine(2023). - Wan, T. K., et al. Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers. BMC Digital Health(2023).
  • 3. Drug Repositioning
    Related publications:
    - R. X. Huang, et al. Lung adenocarcinoma-related target gene prediction and drug repositioning. Front. Pharmacol(2022). - Bennett, A. N., et al. Drug repositioning for esophageal squamous cell carcinoma. Frontiers in Genetics(2022).
  • 4. Analytical / Bioinformatics Tools Development
    Related publications:
    - Bennett, A. N., et al. Canary: an automated tool for the conversion of MaCH imputed dosage files to PLINK files. BMC Bioinformatics(2022).
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