Integrative Analytical Approach for Heterogeneous Genomic Data sets for modeling Complex Diseases
Cancer Genomics
Studying cancer metastasis through integrative computational approaches
Exploratory data analysis techniques for predicting genetic and epigenetic markers in cancers
Tissue specific expression patterns and cell-type specific methylation patterns in cancers
Population Genetics
Evolutionary history of human population
Selection in humans
Application of phylogenetic methods in population genetics
Phylogenetics
Gene trees, species trees reconstruction
Computational Systems Biology
Stem cell differentiation
Transcription factor (TF) binding mechanics
Research Experience
Computational Techniques in Cancer Genomics
currently developing exploratory data analysis techniques for finding genetic and epigenetic markers in primary and metastatic cancer
Study of Shared Pathophysiology of ASD an.d Its Co-morbid Diseases
developed a novel three-tiered meta-analysis pipeline to uncover shared pathways among ASD and its co-occurring diseases.
Integrative Analysis of Heterogeneous Genomic Datasets for Explaining Genetic Etiology of ASDs
developed computational method for integrating data from different sources onto functional networks to facilitate discovery of causal genes and dys-regulated pathways of interest in ASDs.
Integrative Gene Expression Analysis of PCOS
developed a meta analysis pipeline for studying expression data across multiple platforms to identify significant pathways in PCOS.
Study of Functional Genomic Evidence in Positively Selected Human Genome
studied different functional markers in positively selected regions in 1000 genomes data.
Study of Transcription Factor Binding Dynamics during Hematopoetic Stem Cell Differentiation
studied the binding events of Gata1, Gata2, and Smad1 over 13 different timepoints during hematopetic stem cell differentiation.
analyzed differential binding and co-binding of Gata1, Gata2, and Smad1.
Study of the Demographic Relationships of Human Population
applied a quartet-based approach for inferring demographic relationships of minimally admixed human populations.
developed two different pipelines using phylogenetic tree and network construction algorithms in the context of population genetics.
String Inference Algorithms
studied the problem of string inference for both regular and indeterminate strings.
proposed two solutions for the open problem of linear time string inference from cover array using binary alphabet.
extended the classical string inference problem to indeterminate string inference from border arrays and suffix and LCP arrays and devised three novel and efficient algorithms.
Protein Folding in 2D Lattice Models
surveyed all the approximation algorithms for protein folding in 2D and 3D lattice models.
proposed an octagonal 2D lattice model for protein representation.