SVS: data and knowledge integration in computational biology.

In this paper we present a framework for structured variable selection (SVS). The main concept of the proposed schema is to take a step towards the integration of two different aspects of data mining: database and machine learning perspective. The framework is flexible enough to use not only microarray data, but other high-throughput data of choice (e.g. from mass spectrometry, microarray, next generation sequencing). Moreover, the feature selection phase incorporates prior biological knowledge in a modular way from various repositories and is ready to host different statistical learning techniques. We present a proof of concept of SVS, illustrating some implementation details and describing current results on high-throughput microarray data.