Hierarchical Ensemble Methods for Protein Function Prediction. (4th May 2014)

• Record Type: Journal Article
• Authors: Valentini, Giorgio
• Other Names: Can T. Academic Editor.; Hvidsten T. R. Academic Editor.
• Is Part Of: ISRN bioinformatics. Volume 2014(2014)
• Publication Date: 2014-05-04
• Subjects: Bioinformatics -- Periodicals; Computational biology -- Periodicals; Medical informatics -- Periodicals; Computational Biology; Medical Informatics; Bioinformatics; Computational biology; Medical informatics; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2014/901419
• Languages: English
• ISSNs: 2090-7338

Abstract

Abstract : Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware "flat" prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a "consensus" ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research.