A Simple but Robust Complex Disease Classification Method Using Virtual Sample Template

  • Shu-Lin Wang
  • Yaping Fang
  • Jianwen Fang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)


With the advance of high throughput technologies, genomic or proteomic data are accumulated rapidly, demanding robust computational algorithms for large-scale biological data analysis and mining. In this work we propose a simple classification method based on virtual sample template (VST) and three distance measurements. Each VST corresponds to a subclass in training set. The label of a test sample is simply determined by measuring the similarity between the test sample and each VST using the three distance measurements. The test sample is assigned to the subclass of the VST with the minimum distance. Our experimental results indicate that the proposed method is robust in predicative performance. Compared with other common classification methods of complex disease, our method is simpler and often with improved classification performance.


Gene expression profiles autoantibody profiles complex disease classification virtual sample template correlation method 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shu-Lin Wang
    • 1
  • Yaping Fang
    • 1
  • Jianwen Fang
    • 1
  1. 1.Applied Bioinformatics LaboratoryThe University of KansasLawrenceUSA

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