Learning functional linkage networks with a cost-sensitive approach

Assigning functional classes to unknown genes or proteins on diverse large-scale data is a key task in biological systems, and it needs the integration of different data sources and the analysis of functional hierarchies. In this paper we present a method based on Hopfield neural networks which is a variant of a precedent semi-supervised approach that transfers protein functions from annotated to unannotated proteins. Unlike this approach, our method preserves the prior information and takes into account the imbalance between positive and negative examples. To obtain more reliable inferences, we use different evidence sources, and integrate them in a Functional Linkage Network (FLN). Preliminary results show the effectiveness of our approach.