Scalable Combinatorial Tools for Health Disparities Research

Scalable Combinatorial Tools for Health Disparities Research

Despite staggering investments made in unraveling the human genome, current estimates suggest that as much as 90% of the variance in cancer and chronic diseases can be attributed to factors outside an individual’s genetic endowment, particularly to environmental exposures experienced across his or...

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Bibliographic Details
Main Authors: Michael A. Langston, Robert S. Levine, Barbara J. Kilbourne, Gary L. Rogers, Anne D. Kershenbaum, Suzanne H. Baktash, Steven S. Coughlin, Arnold M. Saxton, Vincent K. Agboto, Darryl B. Hood, Maureen Y. Litchveld, Tonny J. Oyana, Patricia Matthews-Juarez, Paul D. Juarez
Format: Article
Language:English
Published: MDPI AG 2014-10-01
Series:International Journal of Environmental Research and Public Health
Subjects:
combinatorial algorithms
data science
graph theoretical techniques
health disparities research
heterogeneous data analysis
high performance computing
public health exposome
relevance networks
scalable computation
Online Access:http://www.mdpi.com/1660-4601/11/10/10419

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http://www.mdpi.com/1660-4601/11/10/10419

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