Anil Jegga

Introduction

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)

Professor

  • 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: Data-driven approaches for drug discovery and drug repositioning involve using computational techniques and large datasets to identify potential drug candidates and repurpose existing drugs for new indications. These approaches can include machine learning algorithms, network analysis, and cheminformatics tools to analyze data from sources such as genomic and proteomic data, clinical trial data, and chemical compound databases. Our aim is to leverage the power of data and computational techniques, to significantly accelerate the drug discovery and development process, as well as identify new uses for existing drugs that may have otherwise gone undiscovered. (Representative publications)

Drug safety 2.0 - Leveraging omics and data analytics: Drug-induced adverse events (AEs) continue to be a major issue in health care. Clinical trial data, the traditional method of identifying and understanding AEs, may not capture the full range of AEs that occur in real-world settings. Real-world data sources and omics technologies, such as RNA sequencing, can provide a more comprehensive view of molecular changes during drug treatment and potentially identify AE biomarkers. While computational approaches can analyze and interpret these complex datasets to identify AE patterns and trends, 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)

Navigating the complexities of idiopathic pulmonary fibrosis (IPF) - Identifying 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. Research on IPF has focused on understanding the disease's causes and developing new treatments. 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, like RNA-seq (bulk and single cell), to study gene expression changes in the lungs of people with IPF. This can help identify potential therapeutic targets. 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

Others

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This page was last updated on January 5, 2023.