Anil Jegga


Our translational bioinformatics lab uses data-driven approaches to solve complex biological questions and improve human health. We specialize in advanced data analysis to uncover hidden insights and advance translational research. Open to all diseases and methods, we strive to make a positive impact through new treatments and therapies. We welcome collaborations and diverse perspectives.

Position & Departmental Affiliation(s)


  • Division of Biomedical Informatics (Cincinnati Children's Hospital Medical Center - CCHMC)
  • Department of Pediatrics (University of Cincinnati - College of Medicine)
  • Department of Biomedical Informatics (University of Cincinnati - College of Medicine)
  • Department of Electrical Engineering and Computer Science (University of Cincinnati, College of Engineering and Applied Science)

Research Areas

Data-driven drug discovery - From ideas to impact: We leverage the power of data (omics and EHR) and computational techniques to significantly accelerate the drug discovery and development process as well as identify new uses for existing drugs (drug repositioning or drug repurposing) that may have otherwise gone undiscovered. We are disease agnostic and our current diseases of interest/focus are IPF, pancreatitis, pulmonary arterial hypertension, port-wine stains, and cardiomyopathy. (Representative publications)

Drug safety - Leveraging omics and data analytics: Drug-induced adverse events (AEs) continue to be a major issue in health care. Clinical trial data fail to capture the full range of AEs that occur in real-world settings. Real-world data sources (EHRs and FAERS) capture these more effectively and coupling these with omics technologies can provide a more comprehensive view of molecular changes during drug treatment and potentially identify AE biomarkers. However, there are still significant gaps in understanding how to predict and prevent drug-induced AEs. We integrate and analyze existing data sources (secondary data analyses) to develop effective approaches for predicting and mitigating drug-induced AEs. (Representative publications)

Deciphering the complexities of idiopathic pulmonary fibrosis (IPF) and discovering biomarkers and novel therapies: IPF is a chronic lung disease that causes scarring in the lungs. It is a leading cause of death and has few treatment options. Despite progress in understanding the causes and developing new treatments, more work is needed to stop or reverse the disease and to find reliable biomarkers for diagnosis, prognosis, and treatment. One promising area of research is using omics data such as RNA-seq (bulk and single cell) to study gene expression changes in the lungs of patients with IPF and use these signatures to query perturbagen databases to identify therapeutic candidates. By analyzing the omics and real-world data (e.g., Electronic health records or the FDA's adverse events), independently and collaboratively, we have been able to identify key pathways and genes that are dysregulated in IPF and may represent potential therapeutic targets. (Representative publications)

Resources and Software



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This page was last updated on July 10, 2024.