Multiple Gene Sets for Cancer Classification Using Gene Range Selection Based on Random Forest

  • Kohbalan Moorthy
  • Mohd Saberi Bin Mohamad
  • Safaai Deris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7802)


The advancement of microarray technology allows obtaining genetic information from cancer patients, as computational data and cancer classification through computation software, has become possible. Through gene selection, we can identify certain numbers of informative genes that can be grouped into a smaller sets or subset of genes; which are informative genes taken from the initial data for the purpose of classification. In most available methods, the amount of genes selected in gene subsets are dependent on the gene selection technique used and cannot be fine-tuned to suit the requirement for particular number of genes. Hence, a proposed technique known as gene range selection based on a random forest method allows selective subset for better classification of cancer datasets. Our results indicate that various gene sets assist in increasing the overall classification accuracy of the cancer related datasets, as the amount of genes can be further scrutinized to create the best subset of genes. Moreover, it can assist the gene-filtering technique for further analysis of the microarray data in gene network analysis, gene-gene interaction analysis and many other related fields.


Gene Selection Cancer Classification Random Forest Gene Expression Microarray Data 


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  1. 1.
    Paz, J.L., Seeberger, P.H.: Recent Advances and Future Challenges in Glycan Microarray Technology. In: Chevolot, Y. (ed.) Carbohydrate Microarrays, vol. 808, pp. 1–12. Humana Press (2012)Google Scholar
  2. 2.
    Pham, T.D., Wells, C., Crane, D.I.: Analysis of Microarray Gene Expression Data. Current Bioinformatics 1, 37–53 (2006)CrossRefGoogle Scholar
  3. 3.
    Liew, A.W.-C., Law, N.-F., Yan, H.: Missing value imputation for gene expression data: computational techniques to recover missing data from available information. Briefings in Bioinformatics 12, 498–513 (2011)CrossRefGoogle Scholar
  4. 4.
    Duval, B., Hao, J.-K.: Advances in metaheuristics for gene selection and classification of microarray data. Briefings in Bioinformatics 11, 127–141 (2010)CrossRefGoogle Scholar
  5. 5.
    Wu, D., Rice, C., Wang, X.: Cancer bioinformatics: A new approach to systems clinical medicine. BMC Bioinformatics 13, 71 (2012)CrossRefGoogle Scholar
  6. 6.
    Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)CrossRefGoogle Scholar
  7. 7.
    Van Steen, K.: Travelling the world of gene–gene interactions. Briefings in Bioinformatics 13, 1–19 (2012)CrossRefGoogle Scholar
  8. 8.
    Hua, J., Tembe, W.D., Dougherty, E.R.: Performance of feature-selection methods in the classification of high-dimension data. Pattern Recogn. 42, 409–424 (2009)MATHCrossRefGoogle Scholar
  9. 9.
    Wong, G., Leckie, C., Kowalczyk, A.: FSR: feature set reduction for scalable and accurate multi-class cancer subtype classification based on copy number. Bioinformatics 28, 151–159 (2012)CrossRefGoogle Scholar
  10. 10.
    Nanni, L., Brahnam, S., Lumini, A.: Combining multiple approaches for gene microarray classification. Bioinformatics 28, 1151–1157 (2012)CrossRefGoogle Scholar
  11. 11.
    Asyali, M.H., Colak, D., Demirkaya, O., Inan, M.S.: Gene Expression Profile Classification: A Review. Current Bioinformatics 1, 55–73 (2006)CrossRefGoogle Scholar
  12. 12.
    Lin, W.-J., Chen, J.J.: Class-imbalanced classifiers for high-dimensional data. Briefings in Bioinformatics (2012)Google Scholar
  13. 13.
    Boulesteix, A.-L., Bender, A., Lorenzo Bermejo, J., Strobl, C.: Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations. Briefings in Bioinformatics 13, 292–304 (2012)CrossRefGoogle Scholar
  14. 14.
    Breiman, L.: Random Forests. Mach. Learn. 45, 5–32 (2001)MATHCrossRefGoogle Scholar
  15. 15.
    Diaz-Uriarte, R., Alvarez de Andres, S.: Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7, 3 (2006)CrossRefGoogle Scholar
  16. 16.
    Moorthy, K., Mohamad, M.S.: Random forest for gene selection and microarray data classification. Bioinformation 7, 142–146 (2011)CrossRefGoogle Scholar
  17. 17.
    Ramaswamy, S., Ross, K.N., Lander, E.S., Golub, T.R.: A molecular signature of metastasis in primary solid tumors. Nature Genetics 33, 49–54 (2003)CrossRefGoogle Scholar
  18. 18.
    van ’t Veer, L.J., Dai, H., van de Vijver, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T., Schreiber, G.J., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R., Friend, S.H.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)CrossRefGoogle Scholar
  19. 19.
    Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences 96, 6745–6750 (1999)CrossRefGoogle Scholar
  20. 20.
    Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  21. 21.
    Singh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., D’Amico, A.V., Richie, J.P., Lander, E.S., Loda, M., Kantoff, P.W., Golub, T.R., Sellers, W.R.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209 (2002)CrossRefGoogle Scholar
  22. 22.
    Efron, B., Tibshirani, R.: Improvements on Cross-Validation: The .632+ Bootstrap Method. Journal of the American Statistical Association 92, 548–560 (1997)MathSciNetMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kohbalan Moorthy
    • 1
  • Mohd Saberi Bin Mohamad
    • 1
  • Safaai Deris
    • 1
  1. 1.Artificial Intelligence & Bioinformatics Research Group, Faculty of Computer Science and Information SystemsUniversiti Teknologi MalaysiaSkudaiMalaysia

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