System Biology in Cancer research
We collaborate with biologists and clinicians to identify interesting biological questions in cancer initiation and development. We use system biology approaches, which understand cancer as an integrated system of genes, proteins, pathways in both cell lines and patient's tumor samples, to study functionally important molecules for cancer diagnosis, prognosis, and treatment responses.
Through interdisciplinary communications and collaborations, we are building a platform from computational analysis to experimental validations and ultimately will benefit for transitional precision medicine in cancer.
Genetic Epidemeology in heart Failure
Heart failure is a prevalent heart disease, which triggered by inherited genetic risk, environmental factors, and interactions between them. We use case-control, family and population-based methods from bioinformatics and biostatistics perspective to learn genetic susceptibility to heart failure related risk factors.
In contrast to many other heart diseases, investigations to identify 'casual' genes associated with etiology factors in heart failure has had limited success in genome-wide association studies, arising more bioinformatics and biostatistics needs in applications. We utilize local Jackson heart study data as the seeds to build a harmonized heart failure platform for research enhancement in this area.
Development of Database and software
Several database development projects are under construction in our lab, including lncRNA breast cancer database, heart failure database, etc. Data are collected and curated from the publically available database and our local collaborators' lab data.
We well designed our database and considered the challenges such as data integration, management, storage and data security. Through our user-friendly web portals, our collaborators can query and search extensive annotations for single gene or group of genes, as well as dynamically run customized bioinformatics analysis such as co-expression analysis, differential gene expression analysis, survival analysis, association studies with clinical variables.