Incorporating Functional Inter-relationships into Protein Function Prediction Algorithms

Abstract

Functional classification schemes that serve as the basis for annotation efforts in several organisms (e.g. the Gene Ontology) are often the source of gold standard information for computational efforts at supervised gene function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the gene-to-function class labels provided by such knowledge bases. For instance, the Gene Ontology not only captures gene annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches. In this paper, we propose to enhance the performance of classification-based protein function prediction algorithms by addressing these issues, using the same inter relationships between functional classes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the k-nearest neighbor classifier. We present experiments on several large genomic data sets, each of which is used for the modeling and prediction of different sets of over hundred classes from the GO Biological Process ontology. The results show that this incorporation produces more accurate predictions for a large number of the functional classes considered, and also that the classes benefitted most by this approach are those containing the fewest members. In addition, we show how our proposed framework can be used for integrating information from the entire GO hierarchy for improving the accuracy of predictions made over a set of base classes.