ACO-based Bayesian network ensembles for the hierarchical classification of ageing-related proteins

The task of predicting protein functions using computational techniques is a major research area in the field of bioinformatics. Casting the task into a classification problem makes it challenging, since the classes (functions) to be predicted are hierarchically related, and a protein can have more than one function. One approach is to produce a set of local classifiers; each is responsible for discriminating between a subset of the classes in a certain level of the hierarchy. In this paper we tackle the hierarchical classification problem in a local fashion, by learning an ensemble of Bayesian network classifiers for each class in the hierarchy and combining their outputs with four alternative methods: a) selecting the best classifier, b) majority voting, c) weighted voting, and d) constructing a meta-classifier. The ensemble is built using ABC-Miner, our recently introduced Ant-based Bayesian Classification algorithm. We use different types of protein representations to learn different classification models. We empirically evaluate our proposed methods on an ageing-related protein dataset created for this research.