  The study of biological processes can greatly benefit from tools that
automatically predict gene functions or directly cluster genes based on shared
functionality. Existing data mining methods predict protein functionality by
exploiting data obtained from high-throughput experiments or meta-scale
information from public databases. Most existing prediction tools are targeted
at predicting protein functions that are described in the gene ontology (GO).
However, in many cases biologists wish to discover functionally related genes
for which GO terms are inadequate. In this paper, we introduce a rich set of
features and use them in conjunction with semisupervised learning approaches in
order to expand an initial set of seed genes to a larger cluster of
functionally related genes. Among all the semi-supervised methods that were
evaluated, the framework of learning with positive and unlabeled examples (LPU)
is shown to be especially appropriate for mining functionally related genes.
When evaluated on experimentally validated benchmark data, the LPU approaches1
significantly outperform a standard supervised learning algorithm as well as an
established state-of-the-art method. Given an initial set of seed genes, our
best performing approach could be used to mine functionally related genes in a
wide range of organisms.
