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Combining dissimilarities in a hyper reproducing kernel hilbert space for complex human cancer prediction

AuthorsMartín-Merino, Manuel; Blanco, Ángela; De Las Rivas, Javier
Issue Date2009
PublisherHindawi Publishing Corporation
CitationJournal of Biomedicine and Biotechnology: 906865 (2009)
AbstractDNA microarrays provide rich profiles that are used in cancer prediction considering the gene expression levels across a collection of related samples. Support Vector Machines (SVM) have been applied to the classification of cancer samples with encouraging results. However, they rely on Euclidean distances that fail to reflect accurately the proximities among sample profiles. Then, non-Euclidean dissimilarities provide additional information that should be considered to reduce the misclassification errors. In this paper, we incorporate in the -SVM algorithm a linear combination of non-Euclidean dissimilarities. The weights of the combination are learnt in a (Hyper Reproducing Kernel Hilbert Space) HRKHS using a Semidefinite Programming algorithm. This approach allows us to incorporate a smoothing term that penalizes the complexity of the family of distances and avoids overfitting. The experimental results suggest that the method proposed helps to reduce the misclassification errors in several human cancer problems. © 2009 Manuel Mart́n-Merino et al.
Description9 páginas, 3 tablas.-- This is an open access article distributed under the Creative Commons Attribution License.
Publisher version (URL)http://dx.doi.org/10.1155/2009/906865
Identifiersdoi: 10.1155/2009/906865
issn: 1110-7243
e-issn: 1110-7251
Appears in Collections:(IBMCC) Artículos
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