Patient care is rapidly evolving towards the inclusion of precision genomic medicine where genomic tests are used by clinicians to determine disease predisposition, prognosis, diagnosis, and improve therapeutic decision-making. The development, deployment, and delivery of genomic tests and results is an intricate process due to lack/evolving genomic data interoperability standards within health information systems. Our team is interested in improving/developing novel genomic data interoperability standards and tools for effective communication and storage of precision genomic medicine test results across molecular pathology, patient care, and translational genomic research.
Our team is specialized in genomics and bioinformatics, where we have made several original contributions in the areas of alternative splicing, comparative genomics, genome sequencing and annotation, genome evolution, genetic marker development, genetic variant calling, and genome wide association studies. Our scientific contributions have been published in high-impact journals, including Science, Nature, PNAS, and Genome Biology, and highlighted by news outlets across the globe. The current focus of my research is in understanding the genetic basis for human cancer health disparities and type 1 diabetes using genomics technologies and clinical metadata.
Cancer impacts all groups of populations. However, it is known that certain racial and ethnic groups may bear a disproportionate burden of cancer compared with other groups resulting in cancer health disparities. My lab develops and applies computational tools to investigate the contribution of host biological factors (e.g., DNA mutations, gene expression, epigenetics, etc) and their interaction with other relevant factors (e.g., diet, environment, microbiome, etc) to cancer health disparities. Our lab is partly funded of the National Cancer Institute (Award # U54 CA233444) through Florida-California Cancer Research, Education & Engagement (CaRE2) Health Equity Center, whose goal is to eliminate cancer health disparities in Florida, California and nationally.
Rapid adoption of electronic health records and standardized representation of healthcare data resulted in adoption of artificial intelligence (AI) to improve patient care. Pathology and Laboratory medicine is no exception to this. Our team applies AI techniques such as machine learning and deep learning to discover and improve clinical outcomes and diagnosis mainly in the context of pathology medicine and genomics .
In collaboration with Dr. Tanmay Lele (at Texas A&M) my team is developing novel nuclear aberration quantification methods to characterize tumors from whole slide pathology images.