- 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)
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Drug Discovery and Drug Repositioning (Representative publications)
Systems Biology of Disease and Drug Response (Representative publications)
Biological Network Analysis (Representative publications)
Gene Regulatory Networks (Representative publications)
Projects (Software and Databases):
- ToppGene Suite
- Orphan Diseasome
- Idiopathic Pulmonary Fibrosis (IPF) - Differential expressed genes database
- 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).
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).
This page was last updated on April 6, 2018