Abstract

The analysis of biomolecular data from high-throughput screens is typically characterized by the high dimensionality of the measured profiles. Development of diagnostic tools for this kind of data, such as gene expression profiles, is often coupled to an interest of users in obtaining interpretable and low-dimensional classification models; as this facilitates the generation of biological hypotheses on possible causes of a categorization. Purely data driven classification models are limited in this regard. These models only allow for interpreting the data in terms of marker combinations, often gene expression levels, and rarely bridge the gap to higher-level explanations such as molecular signaling pathways.

Here, we incorporate into the classification process, additionally to the expression profile data, different data sources that functionally organize these individual gene expression measurements into groups. The members of sucha group of measurements share a common property or characterize a more abstract biological concept. These feature subgroups are then used for the generation of individual classifiers. From the set of these classifiers, subsets are combined to a multi-classifier system. Analysing which individual classifiers, and thus which biological concepts such as pathways or ontology terms, are important for classification, make it possible to generate hypotheses about the distinguishing characteristics of the classes on a functional level.