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Michael Gargano, B.S.

Associate Scientific Software Engineer

Machine Learning, Data-Mining, Full-Stack Web Applications. I aim to bridge the gap between the vast amounts of data and the need for visual, analytical results.

Contact me at: 860-837-2172

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About Michael Gargano, B.S.

I am a software engineer, working to make a difference where biology and computer science meet. I specialize in full-stack web applications, scripting, and bioinformatics related technologies such as bioPerl, bioPython, and NGS frameworks. I have additional interest in machine learning and data mining algorithms. I aim to bridge the gap between the vast amounts of data and the need for results or visualizations.

Education and experience

Education

Northeastern University
M.S., Bioinformatics
2016 - Present

Eastern Connecticut State University
B.S., Computer Science
2012 - 2016

Experience

Computational Sciences, The Jackson Laboratory
Scientific Software Engineer
2017 - Present

Information Management Analytics, Cigna
Software Engineer
2014 - 2017

Selected Publications

Highlighted Abstract

The Gene Expression Omnibus (GEO) is a public repository of gene expression data. Although GEO has its own tool, GEO2R, for data analysis, evaluation of single genes is not straightforward and survival analysis in specific GEO datasets is not possible without bioinformatics expertise. We describe a web application, shinyGEO, that allows a user to download gene expression data sets directly from GEO in order to perform differential expression and survival analysis for a gene of interest. In addition, shinyGEO supports customized graphics, sample selection, data export and R code generation so that all analyses are reproducible. The availability of shinyGEO makes GEO datasets more accessible to non-bioinformaticians, promising to lead to better understanding of biological processes and genetic diseases such as cancer.

6 Dumas J, Gargano MA, Dancik GM. shinyGEO: a web-based application for analyzing gene expression omnibus datasets. Bioinformatics. 2016 Dec 1; 32(23):3679-3681.