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This article is part of the supplement: UT-ORNL-KBRIN Bioinformatics Summit 2010

Open Access Poster presentation

Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data

Gary L Rogers1*, Pablo Moscato2 and Michael A Langston1

Author Affiliations

1 Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA

2 Department of Electrical Engineering and Computer Science, University of Newcastle, NSW, Australia

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BMC Bioinformatics 2010, 11(Suppl 4):P21  doi:10.1186/1471-2105-11-S4-P21


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/11/S4/P21


Published:23 July 2010

© 2010 Rogers et al; licensee BioMed Central Ltd.

Background

The continuing proliferation of high-throughput biological data promises to revolutionize personalized medicine. Confirming the presence or absence of disease is an important goal. In this study, we seek to identify genes, gene products and biological pathways that are crucial to human health, with prostate cancer chosen as the target disease.

Materials and methods

Using case-control transcriptomic data, we devise a graph theoretical toolkit for this task. It employs both innovative algorithms and novel two-way correlations to pinpoint putative biomarkers that classify unknown samples as cancerous or normal.

Results and conclusion

Observed accuracy on real data suggests that we are able to achieve sensitivity of 92% and specificity of 91%.