Abstract
This paper presents an informative gene set selection approach to tumor diagnosis based on the Distance Sensitive Rival Penalized Competitive Learning (DSRPCL) algorithm and redundancy analysis. Since the DSRPCL algorithm can allocate an appropriate number of clusters for an input dataset automatically, we can utilize it to classify the genes (expressed by the gene expression levels of all the samples) into certain basic clusters. Then, we apply the post-filtering algorithm to each basic gene cluster to get the typical and independent informative genes. In this way we can obtain a compact set of informative genes. To test the effectiveness of the selected informative gene set, we utilize the support vector machine (SVM) to construct a tumor diagnosis system based on the express profiles of its genes. It is shown by the experiments that the proposed method can achieve a higher diagnosis accuracy with a smaller number of informative genes and less computational complexity in comparison with the previous ones.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dudoit, D.S., Fridyand, J., Speed, T.P.: Comparison of Discrimination Methods for the Classification of Tumor Using Gene Expression Data. Journal of American Statistical Association 97, 77–87 (2002)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene Selection for Cancer Classification Using Support Vector Machine. Machine Learning 46, 389–422 (2002)
Yu, L., Liu, H.: Redundancy Based Feature Selection for Microarray Data. In: Proceedings of the Tenth ACM Conference on Knowledge Discovery and Data Mining (SIGKDD’04), pp. 737–742 (2004)
Golub, T.R., Slonim, D.K., Tamayo, P., et al.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531–537 (1999)
Ding, C.: Analysis of Gene Expression Profiles: Class Discovery and Leaf Ordering. In: Proceedings of the 6th Annual International Conference on Computational Molecular Biology (RECOMB’02), pp. 127–136 (2002)
Deng, L., Ma, J., Pei, J.: Rank Sum Method for Related Gene Selection and Its Application to Tumor Diagnosis. Chinese Science Bulletin 49, 1652–1657 (2004)
Luo, J., Ma, J.: A Multi-population χ 2 Test Approach to Informative Gene Selection. In: Gallagher, M., Hogan, J.P., Maire, F. (eds.) IDEAL 2005. LNCS, vol. 3578, pp. 406–413. Springer, Heidelberg (2005)
Koller, D., Sahami, M.: Toward Optimal Feature Selection. In: Proceedings of the 13th International Conference on Machine Learning (ICML’96), pp. 284–292 (1996)
Xing, E.P., Jordan, M.I., Karp, R.M.: Feature Selection for High-Dimensional Genomic Microarray Data. In: Proceedings of the 18th International Conference of Machine Learning (ICML’01), pp. 601–608 (2001)
Bo, T., Jonassen, I.: New Feature Subset Selection Procedures for Classification of Expression Profiles. Genome Biology 3, RESEARCH0017.1–0017.11 (2002)
Wang, L., Ma, J.: A Post-Filtering Gene Selection Algorithm Based on Redundancy and Multi-Gene Analysis. International Journal of Information Technology 11, 36–44 (2005)
Ma, J., Wang, T.: A cost-Function Approach to Rival Penalized Competitive Learning (RPCL). IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 36, 722–737 (2006)
Xu, L., Krzyzak, A., Oja, E.: Rival Penalized Competitive Learning for Clustering Analysis, RBF Net, and Curve Detection. IEEE Transactions on Neural Networks 4, 636–649 (1993)
Hsu, C., Chang, C., Lin, C.: A Practical Guide to Support Vector Classification. National Taiwan University. Department of Computer Science and Information Engineering, given in the web: http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
Ben-Dor, A., Friedman, N., Yakhini, Z.: Scoring Genes for Relevance. Agilent Technical Report no.AGL-2000-13 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Ma, J. (2007). Informative Gene Set Selection Via Distance Sensitive Rival Penalized Competitive Learning and Redundancy Analysis. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_143
Download citation
DOI: https://doi.org/10.1007/978-3-540-72383-7_143
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
eBook Packages: Computer ScienceComputer Science (R0)