The mission of RCMI Center for Health Disparities Research (RCHDR) at Jackson State University (JSU) is to develop and implement innovative biomedical, behavioral and/or translational research aimed at improving minority health and ultimately reducing health disparities. The implementation of such research on diseases that disproportionately affect minority and underrepresented populations is expected to generate large amounts of data (big data) in many formats and at many scales. These big data may include high volume of diverse biological, socio-behavioral, and clinical information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points. Consequently, the National Institutes of Health (NIH) has articulated the urgent need to better understand and mine the data to further scientific knowledge and foster innovation and new discovery.
The depth and breadth of large and varied data sources, technologies, and platforms provides a clear opportunity to address health disparities via BDS. However, the advancement of BDS is plagued by the lack of well trained and empowered biomedical researchers and trainees who have access to the technology, tools, and financial resources necessary to address the challenges of BDS. The overarching goal of the RCHDR Biomedical Data Science Training Program (BDS-TP) to train the next generation of scientists to address the monumental challenge associated with the curation, integration, analysis, and interpretation of biomedical big data. With a special emphasis on the use and management of health disparities datasets representing multiple levels and domains of influence articulated in the National Institute of Minority Health and Health Disparities (NIMHD) Research Framework, the above-stated goal of the BDS-TP will be accomplished through two specific aims that include:
- Foster Collaborations between biomedical data scientists and BDS-TP participants and RCHDR investigators.
- Build the capacity of JSU and other Mississippi’s HBCU-Historically Black Colleges and Universities (Alcorn State University, Mississippi Valley State University, and Tougaloo College) in biomedical data science.
Achieving these specific aims will significantly enhance the data science skills of program participants, and enhance their capabilities to foster health disparities research using big data.
BDS LEARNING OBJECTIVES –CORE COMPETENCIES
The primary focus of the BDS program is to train faculty and research associates on the fundamental principles, basic concepts, and technical applications of data science with an emphasis on developing their knowledge and skills in data coding, programming, statistical analysis, and technical applications using biomedical big data. Hence, participants will gain knowledge of the fundamental principles and techniques of data science spanning algorithms, statistics, machine learning, visualization, and data systems in public health and health care. Upon completion, the program participants will achieve the following learning objectives:
- Gain knowledge of the basic principles and concepts of data science
- Learn about the underlying concepts of mathematics, probability and statistics
- Be introduced to statistical analytic techniques utilized in biomedical, socio-behavioral and/or clinical research
- Be introduced to Python programming and gain practical, hands-on experience with Python and related libraries for accessing data from multiple sources and use analytic methods for analyses
- Be introduced to R programming and gain practical, hands-on experience with R and R-Studio for big data analyses
- Understand the concepts of data clustering and practice behind supervised and unsupervised classification
- Be introduced to advanced algorithmic techniques including machine learning and deep learning.
- Be introduced to tools for applied data science using cloud-based platforms for biomedical and clinical research
- Be introduced to all facets of big data analysis, including the extraction, storage, manipulation, and analysis of massive genetic and bioinformatics datasets
- Be introduced to how information contained in databases and data warehouses can be converted into actionable findings using machine learning and other data science techniques
Fall 2021 – Fundamental Concepts and Basic Principles
Summer 2022 – Technical Applications
Azad Bhuiyan, PhD, Professor of Public Health, Department of Epidemiology and Biostatistics, School of Public Health, Jackson State University, Jackson, MS
Allissa Dillman, PhD, Engagement and Outreach Lead, Office of Data Science Strategy, National Institutes of Health, Bethesda, MD
Fazlay Faruque, PhD, Professor and Director of GIS and Remote Sensing Program, School of Population Health, University of Mississippi Medical Center, Jackson, MS
Richard Finley, MD, Professor of Emergency Medicine, University of Mississippi Medical Center, Jackson, MS; Former Director of Medical Analytics for the Center for Telehealth, UMMC Center for Telehealth Jackson, MS.
Chindo Hicks, PhD, Professor & Director of Bioinformatics and Genomics Program, LSU Health Science Center, New Orleans, LA
Sungbum Hong, PhD, Associate Professor of Computer Science, Department of Electrical and Computer Engineering and Computer Science, Jackson State University, Jackson, MS
Hung-Chung Huang, PhD, Assistant Professor, and Leader of Bioinformatics and Computational Biology Laboratory, Department of Biology, Jackson State University, Jackson, MS
Natarajan Meghanathan, PhD, Professor of Computer Science, Department of Electrical & Computer Engineering and Computer Science, Jackson State University, Jackson, MS
Belinda Seto, PhD, Deputy Director, Office of Data Science Strategy. National Institutes of Health, Bethesda, MD
Kurt Showmaker, PhD, Associate Director of Bioinformatics, Molecular and Genomics Core, University of Mississippi Medical Center, Jackson, MS
Paul Tchounwou, ScD, Presidential Distinguished Professor and Director of RCMI Center for Health Disparities Research, Jackson State University, Jackson, MS
Lei Zhang, PhD, Professor of Data Science and Associate Dean for Research and Scholarship School of Nursing University of Mississippi Medical Center, Jackson, MS
Yufeng Zheng, PhD, Professor of Data Science, Department of Data Science, School of Population Health University of Mississippi Medical Center, Jackson, MS