Title: Protein subcelluar localisation prediction with improved performance

Authors: Jing Hu, Changhui Yan

Addresses: Department of Computer Science, Utah State Univerisity, Logan, UT 84322, USA. ' Department of Computer Science, Utah State Univerisity, Logan, UT 84322, USA

Abstract: Predicting the subcellular localisation of proteins is crucial for the determination of protein functions. In this paper, we present a computational method for protein localisation prediction. We start with a simple approach that predicts protein localisation based on Euclidian distance computed from residue composition. Then the performance is gradually improved by introducing a weighted Euclidian distance, including homologous information, and using feature selection. The final method achieves 90.3% accuracy in assigning proteins into five subcellular locations. Comparisons with CELLO, PSORT-B and P-CLASSIFIER show that our method outperforms the others.

Keywords: subcellular localisation; K-nearest neighbour; K-NN; prediction; weighted Euclidean distance; protein functions; protein localisation; protein assignment; proteins.

DOI: 10.1504/IJFIPM.2008.021395

International Journal of Functional Informatics and Personalised Medicine, 2008 Vol.1 No.3, pp.321 - 328

Published online: 22 Nov 2008 *

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