Chirag Patel, PhD
Associate Professor of Biomedical Informatics
Chirag Patel's long-term research goal is to address problems in human health and disease by developing computational and bioinformatics methods to reproducibly and efficiently reason over high-throughput data streams spanning molecules to populations. Patel's group aims to dissect inter-individual differences in human phenomes through strategies that integrate data sources that capture the comprehensive clinical experience (e.g., through the electronic medical record), the complex phenomena of environmental exposure (e.g., high-throughput measures of the exposome), and inherited genomic variation. He received his doctorate in biomedical informatics from Stanford University.
DBMI Research Areas
DBMI Courses
- BMI 704 - Data Science I: Data Science for Medical Decision Making
- BMI 722 - Topics in Translational Biomedical Informatics
Career Opportunities
Reproducible and opposing gut microbiome signatures distinguish autoimmune diseases and cancers: a systematic review and meta-analysis.
Authors: Islam MZ, Tran M, Xu T, Tierney BT, Patel C, Kostic AD.
Microbiome
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Microbiome
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The Joint Public Health Impact of Family History of Diabetes and Cardiovascular Disease among Adults in the United States: A Population-Based Study.
Authors: Rasooly D, Yang Q, Moonesinghe R, Khoury MJ, Patel CJ.
Public Health Genomics
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Public Health Genomics
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Integrative analysis of genomic and exposomic influences on youth mental health.
Authors: Choi KW, Wilson M, Ge T, Kandola A, Patel CJ, Lee SH, Smoller JW.
J Child Psychol Psychiatry
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J Child Psychol Psychiatry
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Reply.
The impact of socioeconomic status on subsequent neurological outcomes in multiple sclerosis.
Authors: Boorgu DSSK, Venkatesh S, Lakhani CM, Walker E, Aguerre IM, Riley C, Patel CJ, De Jager PL, Xia Z.
Mult Scler Relat Disord
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Mult Scler Relat Disord
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Biomimetic Hydrogels in the Study of Cancer Mechanobiology: Overview, Biomedical Applications, and Future Perspectives.
Waist Circumference and Insulin Resistance Are the Most Predictive Metabolic Factors for Steatosis and Fibrosis.
Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images.
Authors: Le Goallec A, Diai S, Collin S, Prost JB, Vincent T, Patel CJ.
Nat Commun
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Nat Commun
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Shared exposure liability of type 2 diabetes and other chronic conditions in the UK Biobank.
Secular Trends in Prevalence of Heart Failure Diagnosis over 20 Years (from the US NHANES).