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    An integrated approach of particle swarm optimization and support vector machine for gene signature selection and cancer prediction

    135370_134215.pdf (318.1Kb)
    Access Status
    Open access
    Authors
    Yeung, C.
    Chan, Kit Yan
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Yeung, C. and Chan, Kit. 2009. An integrated approach of particle swarm optimization and support vector machine for gene signature selection and cancer prediction, in Kozma, R. (ed), 2009 International Joint Conference on Neural Networks (IJCNN 2009), Jun 14 2009, pp. 1728-1734. Georgia, USA: IEEE Press.
    Source Title
    Proceedings of the 2009 international joint conference on neural networks (IJCNN 2009)
    Source Conference
    2009 International Joint Conference on Neural Networks (IJCNN 2009)
    Additional URLs
    http://portal.acm.org/citation.cfm?id=1704425
    ISBN
    9781424435494
    Faculty
    Curtin Business School
    The Digital Ecosystems and Business Intelligence Institute (DEBII)
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    Remarks

    Copyright © 2009 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/40087
    Collection
    • Curtin Research Publications
    Abstract

    To improve cancer diagnosis and drug development, the classification of tumor types based on genomic information is important. As DNA micro array studies produce a large amount of data, expression data are highly redundant and noisy, and most genes are believed to be uninformative with respect to the studied classes. Only a fraction of genes may present distinct profiles for different classes of samples. Classification tools to deal with these issues are thus important. These tools should learn to robustly identify a subset of informative genes embedded in a large dataset that is contaminated with high dimensional noises. In this paper, an integrated approach of support vector machine (SVM) and particle swarm optimization (PSO) is proposed for this purpose. The proposed approach can simultaneously optimize the selection of feature subset and the classifier through a common solution coding mechanism. As an illustration, the proposed approach is applied to search the combinational gene signatures for predicting histologic response to chemotherapy of osteosarcoma patients. Cross validation results show that the proposed approach outperforms other existing methods in terms of classification accuracy. Further validation using an independent dataset shows misclassification of only one out of fourteen patient samples, suggesting that the selected gene signatures can reflect the chemoresistance in osteosarcoma.

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