Neural Computing and Applications

, Volume 21, Issue 4, pp 649–660 | Cite as

Pathway-based microarray analysis for robust disease classification

  • Pitak Sootanan
  • Santitham Prom-on
  • Asawin Meechai
  • Jonathan H. Chan


The advent of high-throughput technology has made it possible to measure genome-wide expression profiles, thus providing a new basis for microarray-based diagnosis of disease states. Numerous methods have been proposed to identify biomarkers that can accurately discriminate between case and control classes. Many of the methods used only a subset of ranked genes in the pathway and may not be able to fully represent the classification boundaries for the two disease classes. The use of negatively correlated feature sets (NCFS) to obtain more relevant features in form of phenotype-correlated genes (PCOGs) and inferring pathway activities is proposed in this study. The two pathway activity inference schemes that use NCFS significantly improved the power of pathway markers to discriminate between two phenotypes classes in microarray expression datasets of breast cancer. In particular, the NCFS-i method provided better contrasting features for classification purposes. The improvement is consistent for all cases of pathways used, using both within- and across-dataset validations. The results show that the two proposed methods that use NCFS clearly outperformed other pathway-based classifiers in terms of both ROC area and discriminative score. That is, the identification of PCOGs within each pathway, especially NCFS-i method, helps to reduce noisy or variable measurements, leading to a high performance and more robust classifier. In summary, we have demonstrated that effective incorporation of pathway information into expression-based disease diagnosis and using NCFS can provide better discriminative and more robust models.


Microarray analysis Disease classification Pathway activity Negatively correlated feature sets Phenotype-correlated genes Discriminative score 



The main author (PS) gratefully acknowledges the financial support from National Research Council of Thailand, School of Information Technology, King Mongkut’s University of Technology Thonburi, as well as Burapha University during his current doctorate study at King Mongkut’s University of Technology Thonburi. PS is especially thankful to Mr. Ponlavit Larpeampaisarl, who helped to implement the script in the work of PCOG identification and activity inference.

Conflict of interests

The authors declare that they have no competing interests.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Pitak Sootanan
    • 1
  • Santitham Prom-on
    • 2
  • Asawin Meechai
    • 3
  • Jonathan H. Chan
    • 4
  1. 1.Individual Based Program (Bioinformatics)King Mongkut’s University of Technology ThonburiBangkokThailand
  2. 2.Department of Computer EngineeringKing Mongkut’s University of Technology ThonburiBangkokThailand
  3. 3.Department of Chemical EngineeringKing Mongkut’s University of Technology ThonburiBangkokThailand
  4. 4.School of Information TechnologyKing Mongkut’s University of Technology ThonburiBangkokThailand

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