An integrated approach of particle swarm optimization and support vector machine for gene signature selection and cancer prediction
MetadataShow full item record
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.
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.
Showing items related by title, author, creator and subject.
Secondary students' understanding of the gene concept : an analysis of conceptual change from multiple perspectives.Venville, Grady J. (1997)A journey into the past century of genetics history reveals transformations of the concept of the gene through notions of discrete units that obeyed Mendelian laws to the modem bewildering gene concept. We can no longer ...
The molecular genetics of human complement C4: implications for mapping MHC disease susceptibility genesPuschendorf, Mareike (2003)The Major Histocompatibility Complex (MHC) is a gene-dense region located on the short arm of chromosome 6 (6p21.31). This region contains the highly polymorphic HLA genes as well as many other genes with immunological ...
Venkatachalam, G.; Kumar, Alan Prem; Sakharkar, K.; Thangavel, S.; Clement, M.; Sakharkar, M. (2011)Peroxisome proliferator-activated receptor (PPAR) belongs to the nuclear hormone receptor superfamily. Recently published reports demonstrate the importance of a direct repeat 2 (DR2) as a PPARγ-responsive element in ...