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  • Poster presentation
  • Open Access

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

BMC Bioinformatics201011 (Suppl 4) :P21

  • Published:


  • Prostate Cancer
  • Gene Product
  • Machine Learning
  • Real Data
  • High Throughput


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%.

Authors’ Affiliations

Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
Department of Electrical Engineering and Computer Science, University of Newcastle, NSW, Australia


© Rogers et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.