Dr. Samuel Wu (MPI, Associate Chair and Professor, Biostatistics) and Dr. Robert Cook (MPI, Professor of Epidemiology and General Internal Medicine) will lead the research team to integrate the disparate data sources maintained by our partners and then utilize the big data to address research questions in treating HIV and alcohol-related morbidity and mortality.
Dr. Fang is an assistant professor in the J. Crayton Pruitt Family Department of Biomedical Engineering and the Director of Smart Medical Informatics Learning and Evaluation (SMILE) Lab. She will lead the efforts in artificial intelligence and machine learning to analyze neuroimaging and multimodal data. She will also be responsible for neuroimage data management and sharing.
The study team also includes Drs. Xiangyang Lou and Zhigang Li from Biostatistics, Yan Wang and Mattia Prosperi from Epidemiology, Shigang Chen from CISE, Robert Leeman from Health Education and Behavior, and Joe Gullet from Clinical & Health Psychology.
The number of persons living with HIV (PLWH) continues to increase in the United States. Alcohol consumption is a significant barrier to both achieving the goal of ending the HIV epidemic and preventing comorbidities among PLWH, as it contributes to both HIV transmission and HIV-related complications.
Recent advances in data capture systems such as medical imaging, and high-throughput biotechnologies make large/complex research and clinical datasets available, including survey data, multi-omics data, electronic medical records, and/or other sources of reliable information related to engagement in care.
The team, will integrate the disparate research data and then utilize novel statistical and AI approaches to address research questions in treating HIV and alcohol-related morbidity and mortality.
This offers the potential of applying “big” data to extract knowledge and insights regarding fundamental physiology, understand the mechanisms by which the pathogenic effects of biotic and abiotic factors are realized and identify potential intervention targets.
Specifically, the researchers will pursue the following three aims: 1) Integrate the disparate data sources through standardization, harmonization, and merging; 2) Develop a web-based data sharing platform including virtual data-sharing communities, data privacy protection, streamlined data approval and access, and tracking of ongoing research activities; 3) Provide statistical support to junior investigators to use the data repository for exploratory data analysis and proposal development.
The researchers will incorporate data science into the T32 training program to support predoctoral and post-doctoral trainees, hoping to expand to include engineering soon. There will also be support for trainees who want to use the data to write any future papers.
The proposed study will tap into disparate data sources, unleash the potential of data and information, accelerate knowledge discovery, advance data-powered health and transform discovery to improve health outcomes for PLWH.