Sparse Canonical Correlation Analysis for Biomarker Discovery : A Case Study in Tuberculosis
Juho Rousu, Daniel D. Agranoff, John Shawe-Taylor and Delmiro Fernandez-Reyes
In: Machine Learning in Systems Biology : Proceedings of the Fifth International Workshop, 20-21 Jul 2011, Vienna, Austria.

Abstract

Biomarker discovery from ’omics data is a challenging task due to the high dimensionality of data and the relative scarcity of sam- ples. Here we explore the potential of canonical correlation analysis, a family of methods that finds correlated components in two views. In particular we use the recently introduced technique of sparse canonical correlation analysis that finds a projection directions that are primally sparse in one of the views and dually sparse in the other view. Our ex- periments show that the method is able to discover meaningful feature combinations that may have use as biomarkers for tuberculosis.

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