Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37741
Title: An integrated approach of particle swarm optimization and support vector machine for gene signature selection and cancer prediction
Authors: Yeung, CW
Leung, FHF 
Chan, KY
Ling, SH
Keywords: Cancer
Digital signatures
Lab-on-a-chip
Medical computing
Particle swarm optimisation
Patient diagnosis
Pattern classification
Support vector machines
Tumours
Issue Date: 2009
Source: Proceedings of the 2009 International Joint Conference on Neural Networks (IJCNN'2009), Atlanta, GA, 14-19 June 2009, p. 3450-3456 How to cite?
Abstract: To improve cancer diagnosis and drug development, the classification of tumor types based on genomic information is important. As DNA microarray 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 chemo-resistance in osteosarcoma.
URI: http://hdl.handle.net/10397/37741
ISBN: 978-1-4244-3548-7
978-1-4244-3553-1 (E-ISBN)
ISSN: 1098-7576
DOI: 10.1109/IJCNN.2009.5178827
Appears in Collections:Conference Paper

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