Mentor Areas
Our research entails the development and application of data integration approach to improve the ability to diagnose, treat, and prevent complex diseases. Our primary focus lies in integrating multi-omics data and biological knowledge to better translate genomic and biomedical data derived from electronic health records (EHR) into clinical products. Our past projects have been both theoretical and applied, and they include developing data integration methods that combine multi-omics data and biological knowledge, predicting clinical outcomes based on interactions between multi-omic features, integrating multi-modal neuroimaging and multi-omics data, and identifying gene-by-environment (GxE) interactions in several phenotypes/diseases. We plan to continue our work in these areas, focusing primarily on providing actionable clinical products based on inter-plays within/between different dimensional genomic data. In particular, our long-term research goal is to develop and evaluate sophisticated data integration methods that simultaneously combine peoples’ individual variations in genomic (‘omic) data, imaging data, phenotype data from EHR, and environment/lifelog data for advancing precision medicine.
Description:
We are looking for highly motivated students to work with our group on developing and evaluating sophisticated data integration methods that simultaneously combine peoples’ individual variations in genomic (‘omic) data, imaging data, phenotype data from EHR, and environment/lifelog data for advancing precision medicine.
Potential projects will include (but not limited to)
- Developing novel bioinformatics or machine learning strategies to integrate multi-omics data in cancer
- Developing novel bioinformatics strategies to integrate biological knowledge and omics data
- Developing an interpretable deep learning approach to integrate omics data
- Developing novel bioinformatics or machine learning strategies to integrate neuroimaging data and omics data in Alzheimer’s disease
- Constructing a disease-disease or gene-gene network using EHR-linked biobank data
- Constructing a comorbidity map using EHR data
More details are available at https://www.biomedinfolab.com/.
Preferred Qualifications
Students should be interested in quantitative research and have a basic quantitative background.
Machine learning or statistical analysis experience using R required and programming abilities such as python or perl is desired.
Details:
Preferred Student Year
First-year, Second-Year, Junior, Senior
Academic Term
Fall, Spring, Summer
I prefer to have students start during the above term(s).Volunteer
Yes
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.