Title: Gene function prediction with knowledge from gene ontology

Authors: Ying Shen; Lin Zhang

Addresses: School of Software Engineering, Tongji University, Shanghai, China ' School of Software Engineering, Tongji University, Shanghai, China

Abstract: Gene function prediction is an important problem in bioinformatics. Due to the inherent noise existing in the gene expression data, the attempt to improve the prediction accuracy resorting to new classification techniques is limited. With the emergence of Gene Ontology (GO), extra knowledge about the gene products can be extracted from GO and facilitates solving the gene function prediction problem. In this paper, we propose a new method which utilises GO information to improve the classifiers' performance in gene function prediction. Specifically, our method learns a distance metric under the supervision of the GO knowledge using the distance learning technique. Compared with the traditional distance metrics, the learned one produces a better performance and consequently classification accuracy can be improved. The effectiveness of our proposed method has been corroborated by the extensive experimental results.

Keywords: gene ontology; semantic similarity; distance learning metric; gene function prediction; gene expression data; bioinformatics; classification accuracy.

DOI: 10.1504/IJDMB.2015.070840

International Journal of Data Mining and Bioinformatics, 2015 Vol.13 No.1, pp.50 - 62

Received: 28 Feb 2014
Accepted: 03 Mar 2014
Published online: 30 Jul 2015 *

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