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An Evaluation of Gene Ranking Methods by Gene Ontology | IEEE Conference Publication | IEEE Xplore

An Evaluation of Gene Ranking Methods by Gene Ontology


Abstract:

In this paper, we use three gene ranking techniques including two new methods based on a single gene score approach and support vector machine (SVM) method based on recur...Show More

Abstract:

In this paper, we use three gene ranking techniques including two new methods based on a single gene score approach and support vector machine (SVM) method based on recursive feature elimination (RFE). These methods have been evaluated on colon cancer dataset by gene ontology. Experimental results showed that these methods select genes that do not have any GO. So, great parts of data that have GO are deposited. With having GO, we can gain a better understanding of the data than just applying statistical analysis because biological significance does not necessarily have to be statistically significant. We propose to enter biological knowledge into gene selection process. Using GO prevents from producing spurious results
Date of Conference: 16-20 November 2006
Date Added to IEEE Xplore: 10 April 2007
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ISSN Information:

Conference Location: Guilin, China
References is not available for this document.

1. Introduction

The DNA microarray technology is emerging recently in the field of computational biology. This technology enables monitoring and measurement of expression levels for thousand of genes simultaneously through the hybridization process [1]. It is commonly used for comparing the gene expression levels in tissues under different conditions, such as wild-type versus mutant, or healthy versus diseased [2]. A typical DNA microarray study utilizes several DNA microarray chips on different tissue samples and generates a numerical array with thousands of rows (genes) and tens of columns (experiments/DNAchips) [4]. The gene expression indicates the approximate amount of protein contents in a given sample [1]. Microarray can be applied to a wide range of studies including gene regulation, disease diagnosis and prognosis, cancer classification, bio-marker discovery and drug development [5].

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1.
Manfred Ng and Laiwan Chan, "Informative Gene Discovery for Cancer Classification from Microarray Expression Data", IEEE, 2005, p.393-398.
2.
Kun Yang, Jianzhong Li, Zhipeng Cai, Guohui Lin, "A Model-Free and Stable Gene Selection in Microarray Data Analysis", Proceedings of the 5th IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05), 2005.
3.
Sach Mukherjee, Stephen J. Roberts, "A Theoretical Analysis of Gene Selection", Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference (CSB 2004)
4.
Xian Xu, Aidong Zhang, "Selecting Informative Genes from Microarray Dataset by Incorporating Gene Ontology", Proceedings of the 5th IEEE Symposium on Bioinformatics and Bioengineering (BIBE'05), 2005.
5.
Songhui Li, Michael J. Becich, John Gilbertson, "Microarray Data Mining Using Gene Ontology", MEDINFO 2004, p.778-782.
6.
Sach Mukherjee, Stephen J. Roberts, "Probabilistic Consistency Analysis for Gene Selection", Department of Engineering Science, University of Oxford, 2005.
7.
Gregory Piatetsky-Shapiro, Pablo Tamayo, "Microarray Data Mining: Facing the Challenges", SIGKDD Explorations. Volume 5, Issue 2, p.1-5, 2005.
8.
Barry Smith, Jennifer Williams and Steffen Schulze-Kremer, "The Ontology of the Gene Ontology", Proceedings of AMIA Symposium 2003.
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11.
http://smd.stanford.edu/cgi-bin/source/sourceBatch_Search.
12.
http://www.freshpatents.com
13.
http://www.geneontology.org
14.
www.oncomine.org
15.
Kai-Bo Duan, Jagath C. Rajapakse, Haiying Wang, and Francisco Azuaje, "Multiple SVM-RFE for Gene Selection in Cancer Classification with Expression Data", IEEE trans. Nanobioscience, vol. 4, no. 3, 2005, p.228-234
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References

References is not available for this document.