Kumardeep Chaudhary
Scientist-E |
Decision Unit 4: Systems Biology
Professor |
Academy of Scientific and Innovative Research
Kumardeep Chaudhary is a computational biologist who works at the interface of artificial intelligence and human genomics to provide translational solutions in the healthcare domain. He comes with rich experience in applied machine learning, genotype-phenotype association analyses, and development of public domain resources and prediction servers. His lab works on multi-modal big data integration including electronic health records (EHRs) in the field of cancer and cardiovascular diseases.
Specific Interests
- AI-based solutions for cardiovascular diseases in South Asian Population
- Identification of robust diagnostic, prognostic and predictive biomarkers in cancer
- Digital and Genomic Medicine for precision healthcare for complex diseases
Kumardeep is the lead author of the study identifying Transthyretin V122I variant associated with heart failure in African American and Hispanic/Latino American population along with subclinical manifestations. Further, he was involved in the development of computational pipeline to identify high risk patients of hepatocellular carcinoma, a predominant form of liver cancer. His lab is committed to making biomedical software, tools and structured datasets for the scientific community and public.
Selected Publications
All Citations →- Exome-wide evaluation of rare coding variants using electronic health records identifies new gene-phenotype associations. Park J, Lucas AM, Zhang X, Chaudhary K, Cho JH, Nadkarni G, et al. Nat Med. 2021;27(1):66–72.
- AKI in Hospitalized Patients with COVID-19. Chan L*, Chaudhary K*, Saha A, Chauhan K, Vaid A, Zhao S, et al. J Am Soc Nephrol. 2021;32(1):151–60. (*joint-first author)
- Association of the V122I Hereditary Transthyretin Amyloidosis Genetic Variant With Heart Failure Among Individuals of African or Hispanic/Latino Ancestry. Damrauer SM*, Chaudhary K*, Cho JH*, Liang LW, Argulian E, Chan L, et al. JAMA. 2019;322(22):2191–202. (*joint-first author)
- Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Chaudhary K, Poirion OB, Lu L, Garmire LX. Clin Cancer Res. 2018;24(6):1248–59.
- Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer. Bhalla S*, Chaudhary K*, Kumar R, Sehgal M, Kaur H, Sharma S, et al. Sci Rep. 2017;7:44997. (*joint-first author)