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Statistical neural networks and support vector machine for the classification of genetic mutations in ovarian cancer

Sehgal, Shoaib; Gondal, Iqbal and Dooley, Laurence (2002). Statistical neural networks and support vector machine for the classification of genetic mutations in ovarian cancer. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB’04), 7-8 Oct 2004, La Jolla, California.

URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb...
DOI (Digital Object Identifier) Link: http://dx.doi.org/10.1109/CIBCB.2004.1393946
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Abstract

An optimal genetic mutation diagnosis requires proper selection of mutation classifier. This work investigates the performance of different classification, missing value estimation (MVE) and data dimension reduction techniques for the classification of gene expression data for BRCA1, BRCA2 and Sporadic mutations of epithelial ovarian cancer. Bayesian MVE and zero imputation techniques were employed to deal with missing values. Our study showed the better performance of the Bayesian technique. A novel approach is introduced to use generalized regression neural network (GRNN) as genetic mutation classifier which completely outperformed both well established support vector machine and probabilistic neural network.

Item Type: Conference Item
Extra Information: ISBN: 0-7803-8728-7
Academic Unit/Department: Mathematics, Computing and Technology > Computing & Communications
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 11357
Depositing User: Laurence Dooley
Date Deposited: 12 Aug 2008 13:34
Last Modified: 02 Dec 2010 20:10
URI: http://oro.open.ac.uk/id/eprint/11357
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