Abstract
Biological data mining has been emerging as a new area of research by incorporating artificial intelligence and biology techniques for automatic analysis of biological sequence data. The size of the biological data collected under the Human Genome Project is growing exponentially. The available data is comprised of DNA, RNA and protein sequences. Automatic classification of protein sequences into different groups might be utilized to infer the structure, function and evolutionary information of an unknown protein sequence. The accurate classification of protein sequences into family/superfamily based on the primary sequence is a very complex and open problem. In this paper, an amino acid position-based feature encoding technique is proposed to represent a protein sequence using a fixed length numeric feature vector. The classification results indicate that the proposed encoding technique with a decision tree classification algorithm has achieved 85.9 % classification accuracy over the Yeast protein sequence dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jeong, J. C., Lin, X. and Chen, X. W.: “On position-specific scoring matrix for protein function prediction,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, pp. 308-315, 2011
Bandyopadhyay, S.: “An efficient technique for superfamily classification of amino acid sequences: Feature extraction, fuzzy clustering and prototype selection,” ELSEVIER Jounal of Fuzzy Sets and Systems, vol. 152, pp. 5-16, 2005
Vipsita, S.,Shee, B. K. and Rath, S. K.: “An efficient technique for protein classification using feature extraction by artificial neural networks IEEE India Conference: Green Energy, Computing and Communication, INDICON 2010
Mansoori, E. G.,Zolghadri, M. J. and Katebi, S. D.:”Protein superfamily classification using fuzzy rule-based classifier,” IEEE Transactions on Nanobioscience, vol. 8, pp. 92-99, 2009
Rossi, A. L. D., & De Oliveira Camargo-Brunetto, M. A.: “Protein classification using artificial neural networks with different protein encoding methods International Conference on Intelligent Systems Design and Applications, ISDA’07, pp. 169-174
Wang, J. T. L.,Ma, Q.,Shasha, D. and Wu, C. H.: “New techniques for extracting features from protein sequences,” IBM Systems Journal, vol. 40, pp. 426-441, 2001
Angadi, U. B. & Venkatesulu, M.: Structural SCOP superfamily level classification using unsupervised machine learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9, 601-608, doi:10.1109/tcbb.2011.114
Ma, P. C. H. & Chan, K. C. C. UPSEC: An algorithm for classifying unaligned protein sequences into functional families. J. Comput. Biol. 15, 431-443, doi:10.1089/cmb.2007.0113 (2008)
Vipsita, S. & Rath, S. K.: Protein superfamily classification using adaptive evolutionary radial basis function network. International Journal of Computational Intelligence and Applications 11, doi:10.1142/s1469026812500265 (2012)
Acknowledgments
The authors would like to thank UNIVERSITI TEKNOLOGI PETRONAS for supporting this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Singapore
About this paper
Cite this paper
Iqbal, M.J., Faye, I., Said, A.M., Samir, B.B. (2014). Data Mining of Protein Sequences with Amino Acid Position-Based Feature Encoding Technique. In: Herawan, T., Deris, M., Abawajy, J. (eds) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Lecture Notes in Electrical Engineering, vol 285. Springer, Singapore. https://doi.org/10.1007/978-981-4585-18-7_14
Download citation
DOI: https://doi.org/10.1007/978-981-4585-18-7_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-4585-17-0
Online ISBN: 978-981-4585-18-7
eBook Packages: EngineeringEngineering (R0)