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Our lab integrates multimodal biomedical data to study human health and diseases.
The current focuses in the lab are RNA systems biology, digital health, and gene-environment interaction.



A new wave of wearable sensors allows frequent and continuous measurements of body functions (physiology), including heart rate, skin temperature, blood oxygen levels, and physical activity. Data from these sensors can reveal personalized differences in daily patterns of activities and provide meaningful and actionable information for human diseases. We apply machine learning technologies to derive health-related insights from big wearable data.



Eukaryotic genomes encode hundreds to thousands of RNA binding proteins (RBPs) with diverse functions in co- and post-transcriptional regulation of RNA metabolism. Previous studies have revealed that RBPs typically have hundreds of targets and multiple RBPs coordinately regulate populations of functionally related mRNAs. We develop novel tools to study RBP binding and function in human health and diseases



Human disease is caused by both the genome and exposome. An individual's genome contains a complete set of the person's genetic material. The variants in this entire six billion base pair collection of individuals' DNA determine the risk baseline of developing metabolic disorders. Additionally, other factors, including diet, stress, lifestyles, behaviors, living environments, and medication, further interact with genetic factors and eventually shape an individual's health. We use both computational and biological tools to study the complex gene-environment interactions in human health and diseases.

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