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SVD Based Gene Selection Algorithm

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

This paper proposes an unsupervised gene selection algorithm based on the singular value decomposition (SVD) to determine the most informative genes from a cancer gene expression dataset. These genes are important for many tasks including cancer clustering and classification, data compression, and samples characterization. The proposed algorithm is designed by making use of the SVD’s clustering capability to find the natural groupings of the genes. The most informative genes are then determined by selecting the closest genes to the corresponding cluster’s centers. These genes are then used to construct a new (pruned) dataset of the same samples but with less dimensionality. The experimental results using some standard datasets in cancer research show that the proposed algorithm can reliably improve performances of the SVD and kmeans algorithm in cancer clustering tasks.

Keywords

Cancer clustering DNA microarray datasets Gene selection algorithm kmeans Singular value decomposition 

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Notes

Acknowledgments

The author would like to thank the reviewers for useful comments. This research was supported by Ministry of Higher Education of Malaysia and Universiti Teknologi Malaysia under Exploratory Research Grant Scheme R.J130000.7828.4L095.

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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Faculty of Computing, N28-439-03Universiti Teknologi MalaysiaJohor BahruMalaysia

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