Combined gene selection methods for microarray data analysis

Hu, Hong and Li, Jiuyong and Wang, Hua and Daggard, Grant (2006) Combined gene selection methods for microarray data analysis. In: 10th International Conference Knowledge-Based Intelligent Information and Engineering Systems, 9-11 Oct 2006, Bournemouth, UK.

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Abstract

[Abstract]: In recent years, the rapid development of DNA Microarray technology has made it possible for scientists to monitor the expression level of thousands of genes in a single experiment. As a new technology, Microarray data presents some fresh challenges to scientists since Microarray data contains a large number of genes (around tens thousands) with a small number of samples (around hundreds). Both filter and wrapper gene selection methods aim to select the most informative genes among the massive data in order to reduce the size of the expression database. Gene selection methods are used in both data preprocessing and classification stages. We have conducted some experiments on different existing gene selection methods to preprocess Microarray data for classification by benchmark algorithms SVMs and C4.5. The study suggests that the combination of filter and wrapper methods in general improve the accuracy performance of gene expression Microarray data classification. The study also indicates that not all filter gene selection methods help improve the performance of classification. The experimental results show that among tested gene selection methods, Correlation Coefficient is the best gene selection method for improving the classification accuracy on both SVMs and C4.5 classification algorithms.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited in accordance with the copyright policy of the publisher. Copyright 2006 Springer. This is the authors' version of the work. It is posted here with permission of the publisher for your personal use. No further distribution is permitted. The item is also available in Lecture Notes in Computer Science v. 4251 at http://www.springerlink.com
Depositing User: Dr Jiuyong (John) Li
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 11 Oct 2007 00:57
Last Modified: 02 Jul 2013 22:42
Uncontrolled Keywords: classification; gene selection; Microarray data
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
06 Biological Sciences > 0604 Genetics > 060405 Gene Expression (incl. Microarray and other genome-wide approaches)
URI: http://eprints.usq.edu.au/id/eprint/2093

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