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Sambhawa Priya, Ph.D.

Chicago DFI Fellow

University of Chicago

About

I am a Chicago DFI Fellow and postdocotral associate at the University of Chicago. Previously, I was a postdoctoral fellow at the Broad Institute of MIT and Harvard. I completed my Ph.D. in Bioinformatics and Computational Biology at the University of Minnesota - Twin Cities.

My research focuses on using machine learning approaches for understanding host-microbiome interactions. I am developing machine learning tools to integrate omics data and electronic health records to shed light on the intricate links between the microbiome and disease. As part of my doctoral research, I developed and applied a machine learning-based framework to perform a comprehensive characterization of interactions between the gut microbiome and host gene expression in patients with colorectal cancer, inflammatory bowel disease, and irritable bowel syndrome.

Interests

  • Bioinformatics
  • Host-microbiome interaction
  • Machine Learning
  • Multi-omics

Education

  • PhD in Bioinformatics and Computational Biology, 2021

    University of Minnesota

  • MS in Computer Science, 2016

    Lehigh University

Selected Publications

See full list at Google Scholar

Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration.

Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome.

Interactions between the gut microbiome and host gene regulation in cystic fibrosis.

Population dynamics of the human gut microbiome: change is the only constant.

Gut microbiota diversity across ethnicities in the United States.

Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers.

Colorectal cancer mutational profiles correlate with defined microbial communities in the tumor microenvironment.

HOMINID: A framework for identifying associations between host genetic variation and microbiome composition.

A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.

Partitioning OWL knowledge bases for parallel reasoning.