Abstract
Identifying the functional characteristic in new annotated proteins is a challenging problem. With the existing sequence similarity search method like BLAST, scope is limited and accuracy is less. Rather than using sequence information alone, we have explored the usage of several composition, hybrid methods, and machine learning to improve the prediction of lipid-transfer proteins. In this paper, we have discussed an approach for genome wide prediction of LTP proteins in rice genome based on amino acid composition using support vector machine (SVM) algorithm. A predictive accuracy of 100 % was obtained for the module implemented with SVM using polynomial kernel. This approach was compared with an All-plant method comprising of six different plants (wheat, maize, barley, arabidopsis, tomato and soybean) which gave an accuracy of only 70 % for SVM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krishnan, P., Ramakrishnan, B., Reddy, K. R., Reddy, V. R.: Chapter three-High-Temperature Effects on Rice Growth, Yield, and Grain Quality. Adv. Agron. 111, 87–206 (2011).
Kader, J. C.: Lipid-transfer proteins in plants. Annu. Rev. Plant. Biol. 47(1),627–654 (1996).
Wang, N. J., Lee, C. C., Cheng, C. S., Lo, W. C., Yang, Y. F., Chen, M. N., Lyu, P. C.: Construction and analysis of a plant non-specific lipid transfer protein database (nsLTPDB). BMC genomics. 13(Suppl 1) (2012).
Liu, Qiang, Yong Zhang, Shouyi Chen.: Plant protein kinase genes induced by drought, high salt and cold stresses. Chinese Sci Bull. 45(13), 1153–1157 (2000).
Li, W., Godzik, A.: Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 22(13,1658–1659 (2006).
Vapnik, V.: The nature of statistical learning theory. Springer Science & Business Media. (2000).
Ma, J., Nguyen, M. N., Rajapakse, J. C.: Gene classification using codon usage and support vector machines. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, 6(1),134–143 (2009).
Altschul, S. F., Madden, T. L., Schffer, A. A., Zhang, J., Zhang, Z., Miller, W., Lipman, D. J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic acids res. 25(17), 3389–3402 (1997).
Schlkopf, B., Burges, C. J. Advances in kernel methods: support vector learning. MIT press (1999).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Hemalatha, N., Narayanan, N.K. (2016). A Support Vector Machine Approach for LTP Using Amino Acid Composition. In: Lobiyal, D., Mohapatra, D., Nagar, A., Sahoo, M. (eds) Proceedings of the International Conference on Signal, Networks, Computing, and Systems. Lecture Notes in Electrical Engineering, vol 396. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3589-7_2
Download citation
DOI: https://doi.org/10.1007/978-81-322-3589-7_2
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-3587-3
Online ISBN: 978-81-322-3589-7
eBook Packages: EngineeringEngineering (R0)