Computational & Systems Biology Program
The Kushal Dey Lab
Research
The Kushal Dey Lab builds statistical and machine learning models that integrate genetic and genomic data to prioritize variants, genes, and cell types, and to decode the causal functional architecture underlying heritable complex diseases — including immune-related diseases, like Alzheimer’s and inflammatory bowel disease, and heritable cancers, like breast cancer.
Nominating candidate risk genes and gene sets underlying disease-critical processes is of utmost importance for developing drug targets and informing CRISPR screening. The Kushal Dey Lab focuses on developing machine learning models and computational pipelines that integrate genomic and epigenomic data from RNA-seq, ChiP-seq, Perturb-seq experiments with genetic association studies (GWAS, WES) to enhance our understanding of the functional architecture of all heritable complex diseases, including immune-related diseases like Alzheimers’, IBD, Lupus and several heritable cancers like Breast and Prostate cancers.
Some of the research directions of interest include developing:
- Models to prioritize variants, genes and cell states for disease using a combination of genetic, genomic and perturbation data.
- Models to identify the causal directed graphs underlying gene and gene interaction models for disease.
- Benchmarking pipelines informed by disease genetics to validate and compare different genomic prediction models.
People
Kushal Dey, PhD
- The Kushal Dey lab focuses on developing machine learning models and computational pipelines that integrate genomic and epigenomic data.
- PhD, University of Chicago
- [email protected]
- Email Address
Members
Computational Biologist
Postdoc
Research Technician
SKI Administrative Assistant II
Postdoc
Lab Affiliations
Achievements
- Josie Robertson Investigator (2023–2028)
- NCI P30 CCSG Developmental Award (2023-2024)
- NCI P30 CCSG supplement – “LLMs in cancer research” (2023-2024)
- Catalog Working Group Co-chair + Disease Focus Group Lead: IGVF consortium (2023-)
- K99/R00 Pathway to Independence Award (NIH/NHGRI) (2022–2026)