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
Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes.
Authors: Lakhani CM, Tierney BT, Manrai AK, Yang J, Visscher PM, Patel CJ.
Nat Genet
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Nat Genet
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Conducting a Reproducible Mendelian Randomization Analysis Using the R Analytic Statistical Environment.
Exposome-wide association study of semen quality: Systematic discovery of endocrine disrupting chemical biomarkers in fertility require large sample sizes.
Publisher Correction: Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma.
Authors: Sagers JE, Brown AS, Vasilijic S, Lewis RM, Sahin MI, Landegger LD, Perlis RH, Kohane IS, Welling DB, Patel CJ, Stankovic KM.
Sci Rep
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Sci Rep
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Trypsin encoding PRSS1-PRSS2 variation influence the risk of asparaginase-associated pancreatitis in children with acute lymphoblastic leukemia: a Ponte di Legno toxicity working group report.
Authors: Wolthers BO, Frandsen TL, Patel CJ, Abaji R, Attarbaschi A, Barzilai S, Colombini A, Escherich G, Grosjean M, Krajinovic M, Larsen E, Liang DC, Möricke A, Rasmussen KK, Samarasinghe S, Silverman LB, van der Sluis IM, Stanulla M, Tulstrup M, Yadav R, Yang W, Zapotocka E, Gupta R, Schmiegelow K.
Haematologica
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Haematologica
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Renal transplant from infant and neonatal donors is a feasible option for the treatment of end-stage renal disease but is associated with increased early graft loss.
Authors: Wijetunga I, Ecuyer C, Martinez-Lopez S, Jameel M, Baker RJ, Welberry Smith M, Patel C, Weston M, Ahmad N.
Am J Transplant
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Am J Transplant
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Using Big Data to Determine Reference Values for Laboratory Tests-Reply.
Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression.
Toward Capturing the Exposome: Exposure Biomarker Variability and Coexposure Patterns in the Shared Environment.
Informatics can help providers incorporate context into care.
