Commenced in January 2007
Paper Count: 31464
Feature Extraction Technique for Prediction the Antigenic Variants of the Influenza Virus
Abstract:In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2021681Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 464
 T. R. Klingen, S. Reimering, C. A. Guzm´an, and A. C. McHardy, “In silico vaccine strain prediction for human influenza viruses,” Trends in Microbiology, vol. 26, no. 2, 2018.
 W. T. Harvey, D. J. Benton, V. Gregory, J. P. Hall, R. S. Daniels, T. Bedford, D. T. Haydon, A. J. Hay, J. W. McCauley, and R. Reeve, “Identification of low-and high-impact hemagglutinin amino acid substitutions that drive antigenic drift of influenza a (h1n1) viruses,” PLoS pathogens, vol. 12, no. 4, p. e1005526, 2016.
 W. T. Harvey, “Quantifying the genetic basis of antigenic variation among human influenza a viruses,” Ph.D. dissertation, University of Glasgow, 2016.
 X. Xia and Z. Xie, “Protein structure, neighbor effect, and a new index of amino acid dissimilarities,” Molecular biology and evolution, vol. 19, no. 1, pp. 58–67, 2002.
 T.-H. Kuo and K.-B. Li, “Predicting protein–protein interaction sites using sequence descriptors and site propensity of neighboring amino acids,” International journal of molecular sciences, vol. 17, no. 11, p. 1788, 2016.
 W. Xue, X.-y. Hong, N. Zhao, R.-l. Yang, and L. Zhang, “Predicting protein subcellular localization by approximate nearest neighbor searching,” in Control And Decision Conference (CCDC), 2017 29th Chinese. IEEE, 2017, pp. 2842–2846.
 M. Fu, Z. Huang, Y. Mao, and S. Tao, “Neighbor preferences of amino acids and context-dependent effects of amino acid substitutions in human, mouse, and dog,” International journal of molecular sciences, vol. 15, no. 9, pp. 15 963–15 980, 2014.
 G.-Z. Wang, L.-L. Chen, and H.-Y. Zhang, “Neighboring-site effects of amino acid mutation,” Biochemical and biophysical research communications, vol. 353, no. 3, pp. 531–534, 2007.
 S. Mallat, A wavelet tour of signal processing. Academic press, 1999.
 P. Lio, “Wavelets in bioinformatics and computational biology: state of art and perspectives,” Bioinformatics, vol. 19, no. 1, pp. 2–9, 2003.
 M. Cardelli, M. Nicoli, A. Bazzani, and C. Franceschi, “Application of wavelet packet transform to detect genetic polymorphisms by the analysis of inter-alu pcr patterns,” BMC bioinformatics, vol. 11, no. 1, p. 593, 2010.
 R. Jiang and H. Yan, “Studies of spectral properties of short genes using the wavelet subspace hilbert–huang transform (wshht),” Physica A: Statistical Mechanics and its Applications, vol. 387, no. 16-17, pp. 4223–4247, 2008.
 J. Ning, C. N. Moore, and J. C. Nelson, “Preliminary wavelet analysis of genomic sequences,” in Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE. IEEE, 2003, pp. 509–510.
 G. Dodin, P. Vandergheynst, P. Levoir, C. Cordier, and L. Marcourt, “Fourier and wavelet transform analysis, a tool for visualising regular patterns in dna,” Journal of Theoretical Biology, vol. 206, no. EPFL-ARTICLE-86700, pp. 323–326, 2000.
 J. Zhao, X. W. Yang, J. P. Li, and Y. Y. Tang, “Dna sequences classification based on wavelet packet analysis,” in Wavelet Analysis and Its Applications. Springer, 2001, pp. 424–429.
 E. R. Dougherty, X. Cai, Y. Huang, S. Kim, and R. Yamaguchi, “Editorial
[hot topic: Genomic signal processing: Part 1 (guest editors: Er dougherty, x. cai, y. huang, s. kim and r. yamaguchi)],” Current Genomics, vol. 10, no. 6, pp. 364–364, 2009.
 H. K. Kwan and S. B. Arniker, “Numerical representation of dna sequences,” in Electro/Information Technology, 2009. eit’09. IEEE International Conference on. IEEE, 2009, pp. 307–310.
 G. K. Hirst, “The quantitative determination of influenza virus and antibodies by means of red cell agglutination,” Journal of Experimental Medicine, vol. 75, no. 1, pp. 49–64, 1942.
 R. Reeve, B. Blignaut, J. J. Esterhuysen, P. Opperman, L. Matthews, E. E. Fry, T. A. De Beer, J. Theron, E. Rieder, W. Vosloo et al., “Sequence-based prediction for vaccine strain selection and identification of antigenic variability in foot-and-mouth disease virus,” PLoS computational biology, vol. 6, no. 12, p. e1001027, 2010.
 D. J. Smith, A. S. Lapedes, J. C. de Jong, T. M. Bestebroer, G. F. Rimmelzwaan, A. D. Osterhaus, and R. A. Fouchier, “Mapping the antigenic and genetic evolution of influenza virus,” science, vol. 305, no. 5682, pp. 371–376, 2004.
 D. K. Ruch and P. J. Van Fleet, Wavelet theory: An elementary approach with applications. John Wiley & Sons, 2011.
 V. Gregory, W. T. Harvey, R. S. Daniels, R. Reeve, L. Whittaker, C. Halai, A. Douglas, R. Gonsalves, J. J. Skehel, A. J. Hay, and J. W. McCauley, “Human former seasonal influenza A(H1N1) haemagglutination inhibition data 1977-2009 from the who collaborating centre for reference and research on influenza – London, UK,” University of Glasgow, Tech. Rep., 2016.
 M. Harman, “The current state and future of search based software engineering,” in 2007 Future of Software Engineering. IEEE Computer Society, 2007, pp. 342–357.
 N. R. Vempaty, V. Kumar, and R. E. Korf, “Depth-first versus best-first search.” in AAAI, 1991, pp. 434–440.