Research Labs
Meharry SACS’s research labs serve as expertise areas that reflect faculty interest and strengths. Labs are tied to the classroom, supporting a practical approach to learning, via individual and group projects, that is important to the School’s curriculum.
The following research labs are intentionally multidisciplinary to align with other wet experimental labs or research interests across Meharry’s schools.
Advanced Computing and Analytics Laboratory | Clinical Trials and Translational Lab | Data-driven Intelligence and Security for Cyber-physical Systems (DISCS) Lab | Genomics Lab | Geographic Information Systems (GIS) and Visualization Lab | L’OMAR – Laboratory of Omics Mining and Algorithmic Reasoning | mHealth Wearable Sensors Lab | Population Health Informatics and Disparities Research Lab (PHIDRL)
Advanced Computing and Analytics Laboratory
The Advanced Computing and Analytics Lab (ACAL) is an open access, gathering and collaborative workspace for student success. In this lab, our students work with faculty, researchers, industry experts and other technology thought leaders.
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.
Data-driven Intelligence and Security for Cyber-physical Systems (DISCS) Lab
Data-driven Intelligence and Security for Cyber-physical Systems (DISCS) Lab, primarily focuses on designing and developing secure AI-based smart cyber-infrastructure for healthcare systems. The lab integrates software-defined networking (SDN), 5G, edge, fog, and cloud technologies along with AI tools for providing efficient and secure data communication and processing for intelligent healthcare and cyber-physical systems.
Genomics Lab
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.
Geographic Information Systems (GIS) and Visualization Lab
The Geographic Information Systems and Visualization Lab applies geospatial and remote sensing data modeling to health care. Faculty explore coverage gaps in population health, climate change effects, and develop algorithms that provide insights and innovative solutions that lead to improved health outcomes, increase accessibility to health care, and healthier communities.
L’OMAR – Laboratory of Omics Mining and Algorithmic Reasoning
The primary focus of L’OMAR is mining omics (aka multi-omics) data such as genomics (DNA), transcriptomics (RNA), proteomics (proteins), metabolomics (metabolites), metagenomics (microbiomes) etc. to understand human biology computationally. L’OMAR develops algorithmic reasoning techniques leveraging artificial intelligence (AI), machine learning, deep learning and natural language processing (NLP) emphasizing on explanations (XAI) to the predictions and decisions an AI-agent may bring in 1) understanding the biology of diseases, 2) identifying promising drug-targets and drug molecules, and 3) repurposing existing drugs for drawing faster response against future pandemic.
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.
Population Health Informatics and Disparities Research Lab (PHIDRL)
The PHIDRL seeks to address health disparities and predict patient health outcomes with the desire to help patient health outcome improvement. The lab pursues health informatics and machine learning pipelines leveraging multi-modality data to identify patient at risk of severe health outcomes. Current research involves substance use disorders, mental health disorders, maternal health, and cardiovascular conditions among underserved patient population, specifically, to identify social determinant of health factors that are associated with adverse health outcomes.