Anil Jegga



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)


Click here for details of current and past students and to find out where our graduates are after their graduation.

Research Areas:

Projects (Software and Databases):


Ongoing Projects

  • Discovery of CFTR and non-CFTR based therapeutics: Current approved treatment for cystic fibrosis (CF) while effective has two major limitations: they show modest improvements in lung function and efficacy decreases with long term use. We are focusing on developing and applying computational big data approaches to integrate and analyze multiple, large and publicly available genome-wide data sets from CF patients and from high-throughput compound screening assays.  In doing so, we plan to systematically identify and prioritize novel candidate therapeutics for CF and validate (in collaboration with Naren Lab, CCHMC) using CF-relevant model systems (CF enteroid-based models) (1UG3TR002612).
  • Integrative analysis of multi-omics data to target fibroblast activation in Idiopathic pulmonary fibrosis (IPF): IPF is an incurable, disabling, and often fatal disease characterized by the distribution of fibrotic lesions predominantly in the peripheral areas of the lung. Currently approved therapies for IPF delay the rate of decline in lung function, but do not halt the progression of fibrosis. We are integrating computational ‘big data’ approaches with experimental validations (in collaboration with Madala Lab, CCHMC) to identify new pre-clinical therapies for IPF (1R21HL133539-01).

  • Discovery and characterization of candidate therapeutics for drug-induced diseases: We are developing and applying data-driven approaches on publicly collected drug-related adverse events from the FDA to identify drugs that are either causative of or protect from drug-induced diseases (DID) or adverse events (AEs). Candidate drugs or drug combinations inversely correlated with DID may have a protective/beneficial role in DID. We will further analyze these candidates through systems biology-based approaches using large scale publicly-available drug-gene expression data to generate mechanism of action and drug repositioning hypotheses (1R21HL135368-01).

Open Positions

Talented individuals (Ph.D. in computer sciences, Informatics, or bioinformatics with a strong interest and/or experience in working on biomedical big data problems) looking for postdoctoral opportunities, please contact us with your current CV, a one-page research statement (or an explanation of your specific interests in my research), and the names and contact information of at least two references.
I also have openings for Ph.D. students to work on projects related to biomedical and genomic big data integration/mining. I am specifically looking for students who have coursework in machine learning, NLP, or artificial intelligence. If you are interested in joining our group, read some of our recent publications and email me with your current CV and an explanation of your specific interests in our research.

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This page was last updated on February 7, 2019