Abstract
Identification of hub proteins solely from amino acids in proteome remains an open problem in computational biology that has been getting increasing deliberations with extensive growth in sequence information. In this context, we have chosen to investigate whether hub proteins can be predicted from amino acid sequence information alone. Here, we propose a novel hub identifying algorithm which relies on the use of conformational, physiochemical and pattern characteristics of amino acid sequences. In order to extract the most potential features, two feature selection techniques, CFS (Correlation-based Feature Selection) and ReliefF algorithms were used, which are widely used in data preprocessing for machine learning problems. The performance of two types of neural network classifiers such as RBF network and multilayer perceptron were evaluated with these filtering approaches. Our proposed model led to successful prediction of hub proteins from amino acid sequences alone with 92.98% and 92.61% accuracy for multilayer perceptron and RBF Network respectively with CFS algorithm and 94.69% and 90.89% accuracy for multilayer perceptron and RBF Network respectively using ReliefF algorithm.
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
Aswathi, B.L., Nair, A.S., Sivasankaran, A., Dhar, P.K.: Identification of hub proteins from sequence. Bioinformation 7 (2011)
Tun, K., Rao, R.K., Samavedham, L., Tanaka, H., Dhar, P.K.: Rich can get poor: conversion of hub to non-hub proteins. Systems and Synthetic Biology 2, 75–82 (2009)
He, X., Zhang, J.: Why do hubs tend to be essential in protein networks? PLoS Genetics 2, e88 (2006)
Patil, A., Kinoshita, K., Nakamura, H.: Hub promiscuity in protein-protein interaction networks. International Journal of Molecular Sciences 11, 1930–1943 (2010)
Hsing, M., Byler, K.G., Cherkasov, A.: The use of Gene Ontology terms for predicting highly-connected “hub” nodes in protein-protein interaction networks. BMC Systems Biology 2, 80 (2008)
Srihari, S.: Detecting hubs and quasi cliques in scale-free networks. In: 2008 19th International Conference on Pattern Recognition, pp. 1–4 (2008)
Dandekar, T., Snel, B., Huynen, M., Bork, P.: Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci. 23, 324–328 (1998)
Overbeek, R., Fonstein, M., D’Souza, M., Pusch, G.D., Maltsev, N.: The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. USA 96, 2896–2901 (1999)
Marcotte, E.M., Pellegrini, M., Ng, H.L., Rice, D.W., Yeates, T.O., Eisenberg, D.: Detecting protein function and protein-protein interactions from genome sequences. Science 285, 751–753 (1999)
Enright, J., Iliopoulos, I., Kyrpides, N.C., Ouzounis, C.A.: Protein interaction maps for complete genomes based on gene fusion events. Nature 402, 86–90 (1999)
Ge, H., Liu, Z., Church, G.M., Vidal, M.: Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat. Genet. 29, 482–486 (2001)
Pellegrini, M., Marcotte, E.M., Thompson, M.J., Eisenberg, D., Yeates, T.O.: Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl. Acad. Sci. USA 96, 4285–4288 (1999)
Kerrien, S., Alam-Faruque, Y., Aranda, B., Bancarz, I., Bridge, A., Derow, C., et al.: IntAct–open source resource for molecular interaction data. Nucleic Acids Research 35, D561–D565 (2007), http://www.ebi.ac.uk/intact/main.xhtml
Apweiler, R., Bairoch, A., Wu, C.H., Barker, W.C., Boeckmann, B., Ferro, S., et al.: Uni-Prot: the Universal Protein knowledgebase. Nucleic Acids Research 9, D115–D119 (2004), http://www.uniprot.org
Jeffrey, H.J.: Chaos game representation of gene structure. Nucleic Acids Res. 18, 2163–2170 (1990)
Kawashima, S., Pokarowski, P., Pokarowska, M., Kolinski, A., Katayama, T., Kanehisa, M.: AAindex: amino acid index database, progress report 2008. Nucleic Acids Research 5, D202–D205 (2008), http://www.genome.jp/aaindex/
Goli, B., Aswathi, B.L., Nair, A.S.: A Novel Algorithm for Prediction of Protein Coding DNA from Non-coding DNA in Microbial Genomes Using Genomic Composition and Dinucleotide Compositional Skew. In: Meghanathan, N., Chaki, N., Nagamalai, D. (eds.) CCSIT 2012, Part II. LNICST, vol. 85, pp. 535–542. Springer, Heidelberg (2012)
Hall, M., Holmes, G.: Benchmarking Attribute Selection Techniques for Discrete Class Data Mining. IEEE Trans. Knowl. Data Eng. 15, 1–16 (2003)
Wang, C., Ding, C., Meraz, R.F., Holbrook, S.R.: PSoL.: A positive sample only learn-ing algorithm for finding non-coding RNA genes. Bioinformatics 22, 2590–2596 (2006)
Liu, H., Yu, L.: Towards integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering 17(3), 1–12 (2005)
Hall, M.A.: Correlation based feature selection for machine learning. Doctoral dissertation, The University of Waikato, Dept. of Comp. Sci. (1999)
Marko, R.S., Igor, K.: Theoretical and empirical analysis of relief and rreliefF. Machine Learning Journal 53, 23–69 (2003)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Proceedings of the Ninth International Workshop on Machine Learning, pp. 249–256. Morgan Kaufmann Publishers Inc. (1992)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
Werbos, P.J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvard University (1974)
Parker, D.B.: Learning-logic. Technical report, TR-47, Sloan School of Management. MIT, Cambridge (1985)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by errorpropagation in Parallel distributed processing. In: Explorations in the Microstructure of Cognition, vol. I. Bradford Books, Cambridge (1986)
Achuthsankar, S.N., Sreenadhan, S.P.: An improved digital fltering technique using nucleotide frequency indicators for locating exons. Journal of the Computer Society of India 36, 60–66 (2006)
Cherian, B.S., Nair, A.S.: Protein location prediction using atomic composition and global features of the amino acid sequence. Biochemical and Biophysical Research Communications 391, 1670–1674 (2010)
Namboodiri, S., Verma, C., Dhar, P.K., Giuliani, A., Nair, A.S.: Sequence signatures of allosteric proteins towards rational design. Systems and Synthetic Biology 4, 271–280 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aswathi, B.L., Goli, B., Nair, A.S. (2013). A Novel Algorithm for Hub Protein Identification in H.Sapiens Using Global Amino Acid Features. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31600-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-31600-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31599-2
Online ISBN: 978-3-642-31600-5
eBook Packages: EngineeringEngineering (R0)