Abstract
In area of bioinformatics, large amount of data is being harvested with functional and genetic features of proteins. The data is being generated consists of thousands of features with least observations instances. In such case, we need computational tools to analyze and extract useful information from vast amount of raw data which help in predicting the major biological functions of genes and proteins with respect to their structural behavior. Thus, in this study, we use a new hybrid approach for features selection and classifying data using Support Vector Machine (SVM) classifiers with Quadratic Discriminant Analysis (QDA) as generative classifiers to increase more performance and accuracy. We compare our results with previous results and seem to be much promising. The proposed method provides the higher recognition ratio rather than other method used in previous studies. The obtained results are also compared with other different classifiers and our hybrid classifiers give more accuracy and achieve better results than any other classifiers.
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
Preview
Unable to display preview. Download preview PDF.
References
Chan, H.S., Dill, K.: The protein folding problem. Physics Today February 24-32 (1993)
Ding, C.H., Dubchak, I.: Multi-class protein folds recognition using support vector machines and neural networks. Bioinformatics 17, 349–358 (2001)
Shen, H.B., Chou, K.C.: Ensemble classifiers for protein fold pattern recognition. Bioinformatics 22, 1717–1722 (2006)
Okun, O.: Protein fold recognition with k-local hyperplane distance nearest neighbor algorithm. In: Proceedings of the Second European Workshop on Data Mining and Text Mining in Bioinformatics, Pisa, Italy, pp. 51–57 (2004)
Nanni, L.: A novel ensemble of classifiers for protein folds recognition. Neuro Computing 69, 2434–2437 (2006)
Eddy, S.R.: Hidden Markov models. Current Opinion in Structural Biology 6, 361–365 (1995)
Madera, M., Gough, J.: A comparison of profile hidden Markov model procedures for remote homology detection. Nucleic Acids Research 30(19), 4321–4328 (2002)
Lampros, C., Papaloukas, C., Exarchos, T.P., Golectsis, Y., IFotiadis, D.: Sequence-based protein structure prediction using a reduced state-space hidden Markov model. Computers in Biology and Medicine 37, 1211–1224 (2007)
Lampros, C., Papaloukas, C., Exarchos, K., IFotiadis, D.: Improving the protein fold recognition accuracy of a reduced state-space hidden Markov model. Computers in Biology and Medicine 39, 907–914 (2009)
Shen, H.B., Chou, K.C.: Hum-mPLoc: an ensemble classifier for large-scale human protein subcellular location prediction by incorporating samples with multiple sites. Biochemical and Biophysical Research Communications 355, 1006–1011 (2007)
Nanni, L., Lumini, A.: MppS: an ensemble of support vector machine based on multiple physicochemical properties of amino acids. Neuro-computing 69, 1688–1690 (2006)
Zhang, C.X., Zhang, J.S.: RotBoost: a technique for combining rotation forest and adaboost. Pattern Recognition Letters 29, 1524–1536 (2008)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a step-wise procedure for building and training a neural network. In: Fogelman, J. (ed.) Neuro-computing: Algorithms, Architectures and Applications. Springer (1990)
Friedman, J.: Another approach to polychotomous classification. Technical report, Department of Statistics, Stanford University (1996)
Krebel, U.: Pair-wise classification and support vector machines. In: Scholkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods —Support Vector Learning, pp. 255–268. MIT Press, Cambridge (1999)
Lin, C.-J.: Formulations of support vector machines: a note from an optimization point of view. Neural Computation 13(2), 307–317 (2001)
Joachims, T.: The Maximum-Margin Approach to Learning Text Classifiers: Methods, Theory, and Algorithms. PhD thesis, Universitaet Dortmund (200)
Yeang, C.-H., Ramaswamy, S., Tamayo, P., Mukherjee, S., Rifkin, R.M., Angelo, M., Reich, M., Lander, E., Mesirov, J., Golub, T.: Molecular classification of multiple tumor types. Bioinformatics: Discovery Note 1(1), 1–7 (2001)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Singh, L., Chetty, G., Sharma, D. (2012). A Hybrid Approach to Increase the Performance of Protein Folding Recognition Using Support Vector Machines. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-31537-4_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31536-7
Online ISBN: 978-3-642-31537-4
eBook Packages: Computer ScienceComputer Science (R0)