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Journal of Biomedicine and Biotechnology
Volume 2009, Article ID 906865, 9 pages
Research Article

Combining Dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction

1Department of Computer Science, Universidad Pontificia de Salamanca (UPSA), C/Compañía 5, 37002 Salamanca, Spain
2Cancer Research Center (CIC-IBMCC, CSIC/USAL), Campus Miguel De Unamuno s/n, 37007 Salamanca, Spain

Received 16 January 2009; Accepted 24 March 2009

Academic Editor: Dechang Chen

Copyright © 2009 Manuel Martín-Merino et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


DNA 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 𝜈 -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.