Yuriy Gusev | Senior Bioinformatics Scientist-lombardi Comprehensive Cancer Center
Georgetown University

Yuriy Gusev, Senior Bioinformatics Scientist-lombardi Comprehensive Cancer Center, Georgetown University

Yuriy Gusev
PhD, Associate Professor, Innovation Center for Biomedical Informatics (ICBI), Georgetown University Medical Center
Dr. Yuriy Gusev has over 20 years of experience in academic and industry research in the fields of  bioinformatics, systems biology, biomarker discovery, and computational modeling of biological systems. He has over 70 peer-reviewed publications in the areas of microRNA, gene, and protein expression profiling in cancer and other human diseases; and quantitative analysis and mathematical modeling of cancer progression and chromosomal instability. He is a Bioinformatics Lead at the Georgetown Innovation Center for Biomedical Informatics (ICBI) and he is responsible for directing the analytical team in the development of pipelines for multi-omics and Next Generation Sequencing data analysis and integration.  His team is involved in several large scale projects including the Georgetown Database of Cancer (GDOC), the In silico Research Center of Excellence, and Georgetown Center for Systems Biology of Cancer as well as external collaborations with NCI, FDA and Argonne National Labs.
Previously, he was a junior faculty member at the Johns Hopkins School of Medicine where he developed novel methodologies for quantitative analysis of cancer biomarkers for breast, pancreatic, liver, prostate, and thyroid cancers. Later on as an assistant professor in bioinformatics at University of Oklahoma he was a co-director of the bioinformatics core and worked on multiple NIH funded projects involving a computational analysis of microRNA and mRNA profiling data in breast, pancreatic and liver tumors, cell lines and animal models; and he has also developed a novel methodology for functional profiling of co-expressed microRNAs.


Microbiome World Congress USA Day 1 2017 @ 12:04

Roundtable 4: Bioinformatics: Utilizing and translating microbiome data sets


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