Abstract
Protein secondary structure prediction problem is one of the widely studied problems in bioinformatics. Predicting the secondary structure of a protein is an important step for determining its tertiary structure and thus its function. This paper explores the protein secondary structure problem using a novel feature selection algorithm combined with a machine learning approach based on random forests. For feature reduction, we propose an algorithm that uses a graph theoretical approach which finds cliques in the non-position specific evolutionary profiles of proteins obtained from BLOSUM62. Then, the features selected by this algorithm are used for condensing the position specific evolutionary information obtained from PSI-BLAST. Our results show that we are able to save significant amount of space and time and still achieve high accuracy results even when the features of the data are 25% reduced.
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References
Altschul, S.F., et al.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25(17), 3389–3402 (1997)
Altun, G., et al.: Hybrid SVM kernels for protein secondary structure prediction. In: Proc. IEEE Intl Conf. on Granular Computing (GRC 2006), pp. 762–765 (2006)
Aydin, Z., Altunbasak, Y., Borodovsky, M.: Protein secondary structure prediction for a single-sequence using hidden semi-Markov models. BMC Bioinformatics 7, 178 (2006)
Berman, H., et al.: The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB Data.
Birzele, F., Kramer, S.: A new representation for protein secondary structure prediction based on frequent patterns. Bioinformatics 22(21), 2628–2634 (2006)
Butenko, S., Wilhelm, W.: Clique-detection models in computational biochemistry and genomics. European Journal of Operational Research, To appear (2006), Available online at http://www.sciencedirect.com/
Breiman, L.: Random Forests. Machine Learning 45, 15–32 (2001)
Breiman, L., Cutler, A.: Random Forest, http://www.stat.berkeley.edu/~breiman/RandomForests/cc_software.htm
Bystroff, C., Thorsson, V., Baker, D.: HMMSTR: a Hidden Markov Model for Local Sequence Structure Correlations in Proteins. J. Mol. Biol. 301, 173–190 (2000)
Chou, P.Y., Fasman, G.D.: Prediction of protein conformation. Biochemistry 13(2), 222–245 (1974)
Efron, B., Tibshirani, R.: An Introduction to the Bootstrap. Chapman and Hall, New York (1993)
Fleming, P.J., Gong, H., Rose, G.D.: Secondary structure determines protein topology. Protein Science 15, 1829–1834 (2006)
Garnier, J., Osguthorpe, D.J., Robson, B.: Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J. Mol. Biol. 120, 97–120 (1978)
Henikoff, S., Henikoff, J.G.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. 89, 10915–10919 (1992)
Hu, H., et al.: Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier. IEEE Trans. NanoBiosci. 3, 265 (2004)
Hua, S., Sun, Z.: A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach. J. Mol. Biol 308, 397–407 (2001)
Jones, D.T.: Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999)
Karypis, G.: YASSPP: better kernels and coding schemes lead to improvements in protein secondary structure prediction. Proteins 64(3), 575–586 (2006)
Kloczkowski, A., et al.: Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence. Proteins 49, 154–166 (2002)
Kim, H., Park, H.: Protein Secondary Structure based on an improved support vector machines approach. Protein Eng. (2003)
Kurgan, L., Homaeian, L.: Prediction of Secondary Protein Structure Content from Primary Sequence Alone-A Feature Selection Based Approach. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS (LNAI), vol. 3587, pp. 334–345. Springer, Heidelberg (2005)
Niskanen, S., Östergård, P.R.J.: Cliquer User’s Guide, Version 1.0. Communications Laboratory, Helsinki University of Technology, Espoo, Finland, Tech. Rep. T48 (2003)
Östergård, P.R.J.: A fast algorithm for the maximum clique problem. Discrete Applied Mathematics 120(1-3), 197–207 (2002)
Przytycka, T., Aurora, R., Rose, G.D.: A protein taxonomy based on secondary structure. Nature Structural Biol. 6, 672–682 (1999)
Przybylski, D., Rost, B.: Alignments grow, secondary structure prediction improves. Proteins 46, 197–205 (2002)
Rost, B.: Rising accuracy of protein secondary structure prediction. In: Chasman, D. (ed.) Protein structure determination, analysis, and modeling for drug discovery, pp. 207–249. Dekker, New York (2003)
Rost, B., Sander, C.: Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 232, 584–599 (1993)
Shi, S.Y.M., Suganthan, P.N.: Feature Analysis and Classification of Protein Secondary Structure Data. In: Kaynak, O., et al. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 1151–1158. Springer, Heidelberg (2003)
Su, C.-T., Chen, C.-Y., Ou, Y.-Y.: Protein disorder prediction by condensed PSSM considering propensity for order or disorder. BMC Bioinformatics 7, 319 (2006)
Vishveshwara, S., Brinda, K.V., Kannan, N.: Protein Structure: Insights from Graph Theory. J. Th. Comp. Chem. 1, 187–211 (2002)
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Altun, G., Hu, HJ., Gremalschi, S., Harrison, R.W., Pan, Y. (2007). A Feature Selection Algorithm Based on Graph Theory and Random Forests for Protein Secondary Structure Prediction. In: Măndoiu, I., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2007. Lecture Notes in Computer Science(), vol 4463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72031-7_54
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DOI: https://doi.org/10.1007/978-3-540-72031-7_54
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