We develop methods for integrating thousands of publicly available –omics datasets to build genome-scale models of how genes interact with each other in various biological contexts including health and disease.

Much genetic/molecular knowledge is buried within millions of published papers. We develop methods to automatically extract links between biological entities from literature while complementing manual curation.

Most diseases vary in prevalence and impact between females/males and across life stages. We are developing methods to delineate the genomic basis of differences in physiology and disease between sexes and across ages.

Single-cell omics gives unprecedented resolution into gene expression regulatory programs. We are developing methods to use these data to study cell-type and developmental specificity of complex diseases.

We develop new ML approaches to predict novel genes related to functions and diseases by using prior knowledge of various types and varied confidence levels to constraint model structure and train model parameters.

Massive public omics datasets contain several "holes" – not just "missing" but systematically unmeasured values. We are developing statistical methods to impute these values to enable integrative analyses. 

Each disease is not a single well-defined condition. We are working on building integrative approaches that can unravel subtypes of complex disorders defined by common functional/mechanistic deregulations.

Given the prohibitive costs and timelines for developing new drugs, we are working on machine learning approaches to repurposing approved drugs and drug combinations for complex and infectious diseases.

Our group is committed to making all our computational methods available to the broader research community in the form of software and online tools to promote open science, i.e., transparency, reproducibility, and reuse.

There are nearly 2 million publicly available omics samples but they remain acutely underused. We are developing methods to systematically annotate these samples to enable researchers to find and reuse them.

Choosing the right in vivo model system to study human biology is hard. We are working on methods to translate omics data and knowledge between human and models for studying specific facets of complex traits and diseases.

Effectively communicating science and medicine is as critical as making new discoveries. We are developing new methods to quantify language understandability and tools for science/ medical communication & education.