An empirical evaluation of the effectiveness of different types of predictor attributes in protein function prediction

Abstract

Many classification schemes for defining protein functions, such as Gene Ontology (GO), are organised in a hierarchical structure. Nodes near the root of the hierarchy represent general functions while nodes near the leaves of the hierarchy represent more specific functions, giving the flexibility to specify at which level the protein will be annotated. In a data mining perspective, hierarchical structures present a more challenging problem, since the relationship between nodes need to be considered. This chapter presents an empirical evaluation of different protein representations for protein function prediction in terms of maximizing predictive accuracy, investigating which type of representation is more suitable for different levels of the GO hierarchy. © 2009 Springer-Verlag Berlin Heidelberg.