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The transcriptomics of cancer tumors are characterized with tens of thousandsof gene expression features. Patient prognosis or tumor stage can be assessedby machine learning techniques like supervised classification tasks given agene expression profile. Feature selection is a useful approach to select thekey genes which helps to classify tumors. In this work we propose a featureselection method based on Multiple Kernel Learning that results in a reducedsubset of genes and a custom kernel that improves the classificationperformance when used in support vector classification. During the featureselection process this method performs a novel latent regularisation byrelaxing the supervised target problem by introducing unsupervised structureobtained from the latent space learned by a non linear dimensionality reductionmodel. An improvement of the generalization capacity is obtained and assessedby the tumor classification performance on new unseen test samples when theclassifier is trained with the features selected by the proposed method incomparison with other supervised feature selection approaches.

Latent regularization for feature selection using kernel methods in tumor classification