Nita Parekh

Research Interest

• COVID-19


• Demographic analysis of SARS-COV-2 mutations
• Analysis of chest X-rays and CT-scan images using machine learning approaches for diagnosis of COVID-19


• Cancer Genomics


• Genetic variation analysis (CNVs and SSVs) in DLBCL using next generation sequence (NGS) data
• Integration of DNA methylation, differential gene expression and variation data for identifying diagnostic and prognostic markers in breast cancer
• Machine learning (ML) approaches for cancer subtype classification using transcriptomics and methylation data


• Biological Network Analysis


• Protein Contact Networks (residue-residue interaction networks) for detection of structural repeats, residues involved in protein-protein/DNA/RNA interactions, domain identification, etc.
• Gene co-expression network analysis of abiotic and biotic stress conditions in rice (microarray and RNASeq data) for identifying stress-specific biomarkers.