Chirag Patel, PhD

Chirag Patel, PhD

Associate Professor of Biomedical Informatics

10 Shattuck Street, Boston, MA 02115

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
Career Opportunities

Postdoctoral Fellows: Single Cell Transcriptomics/Aging

What about the environment? Leveraging multi-omic datasets to characterize the environment's role in human health.
Authors: Passero K, Setia-Verma S, McAllister K, Manrai A, Patel C, Hall M.
Pac Symp Biocomput
View full abstract on Pubmed
Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes.
Authors: Cromer SJ, Lakhani CM, Wexler DJ, Burnett-Bowie SM, Udler M, Patel CJ.
medRxiv
View full abstract on Pubmed
A multi-omic analysis of birthweight in newborn cord blood reveals new underlying mechanisms related to cholesterol metabolism.
Authors: Alfano R, Chadeau-Hyam M, Ghantous A, Keski-Rahkonen P, Chatzi L, Perez AE, Herceg Z, Kogevinas M, de Kok TM, Nawrot TS, Novoloaca A, Patel CJ, Pizzi C, Robinot N, Rusconi F, Scalbert A, Sunyer J, Vermeulen R, Vrijheid M, Vineis P, Robinson O, Plusquin M.
Metabolism
View full abstract on Pubmed
Clinical spectrum, prognosis and estimated prevalence of DNAJB11-kidney disease.
Authors: Huynh VT, Audrézet MP, Sayer JA, Ong AC, Lefevre S, Le Brun V, Després A, Senum SR, Chebib FT, Barroso-Gil M, Patel C, Mallett AJ, Goel H, Mallawaarachchi AC, Van Eerde AM, Ponlot E, Kribs M, Le Meur Y, Harris PC, Cornec-Le Gall E.
Kidney Int
View full abstract on Pubmed
Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services.
Authors: Krissaane I, De Niz C, Gutiérrez-Sacristán A, Korodi G, Ede N, Kumar R, Lyons J, Manrai A, Patel C, Kohane I, Avillach P.
J Am Med Inform Assoc
View full abstract on Pubmed
Feasibility of Ultra-Rapid Exome Sequencing in Critically Ill Infants and Children With Suspected Monogenic Conditions in the Australian Public Health Care System.
Authors: Lunke S, Eggers S, Wilson M, Patel C, Barnett CP, Pinner J, Sandaradura SA, Buckley MF, Krzesinski EI, de Silva MG, Brett GR, Boggs K, Mowat D, Kirk EP, Adès LC, Akesson LS, Amor DJ, Ayres S, Baxendale A, Borrie S, Bray A, Brown NJ, Chan CY, Chong B, Cliffe C, Delatycki MB, Edwards M, Elakis G, Fahey MC, Fennell A, Fowles L, Gallacher L, Higgins M, Howell KB, Hunt L, Hunter MF, Jones KJ, King S, Kumble S, Lang S, Le Moing M, Ma A, Phelan D, Quinn MCJ, Richards A, Richmond CM, Riseley J, Rodgers J, Sachdev R, Sadedin S, Schlapbach LJ, Smith J, Springer A, Tan NB, Tan TY, Temple SL, Theda C, Vasudevan A, White SM, Yeung A, Zhu Y, Martyn M, Best S, Roscioli T, Christodoulou J, Stark Z.
JAMA
View full abstract on Pubmed
Metabolites, Nutrients, and Lifestyle Factors in Relation to Coffee Consumption: An Environment-Wide Association Study.
Authors: Elhadad MA, Karavasiloglou N, Wulaningsih W, Tsilidis KK, Tzoulaki I, Patel CJ, Rohrmann S.
Nutrients
View full abstract on Pubmed
Prediction of chronological and biological age from laboratory data.
Authors: Sagers L, Melas-Kyriazi L, Patel CJ, Manrai AK.
Aging (Albany NY)
View full abstract on Pubmed
Frequency of Sexually Transmitted Infection/HIV Testing Among Commercially Insured Patients With International Classification of Disease Tenth Revision Specified Sex Partners.
Authors: Kumar S, Patel C, Tao G.
Sex Transm Dis
View full abstract on Pubmed
A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.
Authors: Le Goallec A, Tierney BT, Luber JM, Cofer EM, Kostic AD, Patel CJ.
PLoS Comput Biol
View full abstract on Pubmed