Research in SACS is conducted in various computer labs built to reflect faculty interests and strengths. The following research labs are intentionally multidisciplinary to align with other wet experimental labs or research interests across Meharry’s schools.
The focus of the genomics lab is to develop novel next-generation sequencing methods to identify genomic aberrations that cause disease or drug resistance and use our expertise to serve Meharry and the broader scientific community. An open-source tool Dr. Qingguo Wang developed in the past is VirusFinder, which is the first fully automatic software for characterizing integration sites of undiagnosed viruses of arbitrary types through sequencing data. VirusFinder together with our other software have greatly empowered the ability of scientists to investigate the etiologic association of genomic aberrations with complex human diseases. Below is a schematic illustration of how VirusFinder works.
Geographic Information Systems (GIS) and Visualization Lab
This lab is equipped with Esri’s ArcGIS Platform. It uses geospatial and visualization technologies to underscore coverage gaps in population health, and to develop algorithms that provide insights and innovative solutions that lead to improved health outcomes, increased accessibility to health care, and healthier communities.
Population Health Informatics and Disparities Research Lab (PHIDRL)
Dr. Aize Cao concentrates on applying statistics and machine learning technologies to leverage big electronic health records (EHR) and informatics to improve understanding of health disparity as well as individual and population health. The lab is working on developing and utilizing statistical tools and integrating those resources to increase data integrity and completeness to improve patient health outcome prediction and visualization.
Dr. Cao is working to develop phenotype risk adjustment models for COVID-19 and is pursuing an NIH grant for that study. She is also extending her work on a Common Data Model (CDM) so that underserved populations are well represented in these network studies using CDMs like OMOP (Observational Health Data Sciences and Informatics). She teaches courses in statistical inference, health care informatics and predictive modeling. A complete list of her publications is available online.
mHealth Wearable Sensors Lab
The focus of mHealth Wearable Sensors Lab is to design mHealth systems and develop computational methods for efficient processing of multimodal data streams generated from these systems, to produce early insights of life-threating diseases, and provide personalized care to patients. The overarching goal of this lab is to leverage the power of digital health devices (i.e., mHealth apps, wearable devices) to measure an individual’s physiological, psychological, social and environmental state and utilize that information for early diagnosis and prevention of complex human diseases.
Dr. Vibhuti Gupta is using novel computational techniques he developed in the past to analyze high frequency data captured from wearable sensor devices for the early detection of adverse clinical events in hematologic cancer patients. Currently, Dr. Gupta is leveraging the mHealth wearable sensors data for early diagnosis and prediction of COVID-19. Below is a schematic illustration of Dr. Gupta’s current project on mHealth wearable sensor data analytics.
Clinical Trials and Translational Lab
Dr. Ashutosh Singhal is working in partnership with Dr. Rajbir Singh, interim director of the Clinical and Translational Research Center, to develop and test hypotheses on human subjects under controlled environments, and translate them into strategies for improving health care delivery, patient outcomes and community health. Singhal is also building machine learning tools for the prediction of chronic diseases in underserved and minority populations to develop clinical decision support and self-management tools to improve patient care and outcomes.
Biomedical Imaging Analysis Lab
The biomedical image analysis lab works with Meharry biologists, chemists, and physicians to develop methodology for the examination of biological and physical structure to help with basic research and clinical decision support. The lab also focuses on the applications of state-of-the-art machine learning and deep learning tools to improve the image-based disease diagnostics. For example, neuroimage processing and the analysis of magnetic resonance images can be used to measure brain structure and function in helping with mental disease study. The process to make a diagnostic decision by viewing biomedical imaging is labor intensive and erroneous. By automating the imaging-based diagnostics, labor cost can be cut and more importantly the accuracy of diagnostics can be significantly improved.
Medical Data Security Lab
The most sensitive information about a person is often their health information. Individuals, health care providers, researchers and other stakeholders alike have significant concerns about the confidentiality and integrity of electronic health information that is created, transmitted and stored using health IT. This is especially true in light of the health care industry’s move toward cloud-based storage, where patient data of entire populations is held in one place.
Research done in this lab focusses on the challenges facing health care data security today. These issues include cybersecurity attacks, breaches, hacking and other threats; risks associated with health information exchanges; user errors in technology adoption and transmission of unencrypted medical data; risks of the adopting cloud and mobile technology in health care; and the use of legacy technology in medical facilities. The lab will develop robust mechanisms and best practices for securing information so that health care organizations can continue to advance with interoperable systems.
Drug Discovery Lab
SACS is working in partnership with BERG, Regeneron, and the Department of Energy’s Oak Ridge National Laboratory (ORNL) to tap the revolutionary Summit supercomputer at the ORNL for rapid drug discovery research in the fight against COVID-19.