Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks

References

• WHO. Influenza (Seasonal). Geneva: World Health Organization; 2016.
   Google Scholar
• Blackburne BP, Hay AJ, Goldstein RA. Changing selective pressure during antigenic changes in human influenza H3. PLoS Pathog. 2008;4(5):e1000058. doi:10.1371/journal.ppat.1000058.
   PubMed Web of Science ®Google Scholar
• Chan C-M, Chu H, Zhang AJ, Leung L-H, Sze K-H, Kao RYT, Chik KKH, To KKW, Chan JFW, Chen H, et al. Hemagglutinin of influenza A virus binds specifically to cell surface nucleolin and plays a role in virus internalization. Virology. 2016;494:78–88. doi:10.1016/j.virol.2016.04.008.
   PubMed Web of Science ®Google Scholar
• Kageyama T, Fujisaki S, Takashita E, Xu H, Yamada S, Uchida Y, Neumann G, Saito T, Kawaoka Y, Tashiro M. Genetic analysis of novel avian A (H7N9) influenza viruses isolated from patients in China, February to April 2013. Euro Surveill. 2013;18(15):7–21.
   Google Scholar
• Holmes EC, Ghedin E, Miller N, Taylor J, Bao Y, St George K, Grenfell BT, Salzberg SL, Fraser CM, Lipman DJ, et al. Whole-genome analysis of human influenza A virus reveals multiple persistent lineages and reassortment among recent H3N2 viruses. PLoS Biol. 2005;3(9):e300. doi:10.1371/journal.pbio.0030300.
   PubMed Web of Science ®Google Scholar
• De A, Nandy A. An insight to segment based genetic exchange in Influenza A virus: an in silico study. Basel (Switzerland); 2015. doi:10.3390/MOL2NET-1-b015.
   Google Scholar
• Peng D, Hu S, Hua Y, Xiao Y, Li Z, Wang X, Bi D. Comparison of a new gold-immunochromatographic assay for the detection of antibodies against avian influenza virus with hemagglutination inhibition and agar gel immunodiffusion assays. Vet Immunol Immunopathol. 2007;117(1):17–25. doi:10.1016/j.vetimm.2007.01.022.
   PubMed Web of Science ®Google Scholar
• Stephenson I, Wood J, Nicholson K, Zambon M. Sialic acid receptor specificity on erythrocytes affects detection of antibody to avian influenza haemagglutinin. J Med Virol. 2003;70(3):391–98. doi:10.1002/()1096-9071.
   PubMed Web of Science ®Google Scholar
• Sun H, Yang J, Zhang T, Long L-P, Jia K, Yang G, Webby RJ, Wan X-F. Using sequence data to infer the antigenicity of influenza virus. MBio. 2013;4(4):e00230–00213. doi:10.1128/mBio.00230-13.
   PubMed Web of Science ®Google Scholar
• Ren X, Li Y, Liu X, Shen X, Gao W, Li J. Computational identification of antigenicity-associated sites in the hemagglutinin protein of a/h1n1 seasonal influenza virus. PLoS One. 2015;10(5):e0126742. doi:10.1371/journal.pone.0126742.
   PubMed Web of Science ®Google Scholar
• Yao Y, Li X, Liao B, Huang L, He P, Wang F, Yang J, Sun H, Zhao Y, Yang J. Predicting influenza antigenicity from Hemagglutinin sequence data based on a joint random forest method. Sci Rep. 2017;7. doi:10.1038/s41598-017-01699-z.
   Web of Science ®Google Scholar
• Liao Y-C, Lee M-S, Ko C-Y, Hsiung CA. Bioinformatics models for predicting antigenic variants of influenza A/H3N2 virus. Bioinformatics. 2008;24(4):505–12. doi:10.1093/bioinformatics/btm638.
   PubMed Web of Science ®Google Scholar
• Pan K, Subieta KC, Deem MW. A novel sequence-based antigenic distance measure for H1N1, with application to vaccine effectiveness and the selection of vaccine strains. Protein Eng Des Sel. 2011;24(3):291–99. doi:10.1093/protein/gzq105.
   PubMed Web of Science ®Google Scholar
• Steinbrück L, Klingen T, McHardy A. Computational prediction of vaccine strains for human influenza A (H3N2) viruses. J Virol. 2014;88(20):12123–32. doi:10.1128/JVI.01861-14.
   PubMed Web of Science ®Google Scholar
• Lees WD, Moss DS, Shepherd AJ. A computational analysis of the antigenic properties of haemagglutinin in influenza A H3N2. Bioinformatics. 2010;26(11):1403–08. doi:10.1093/bioinformatics/btq160.
   PubMed Web of Science ®Google Scholar
• Lee M-S, Chen M-C, Liao Y-C, Hsiung CA. Identifying potential immunodominant positions and predicting antigenic variants of influenza A/H3N2 viruses. Vaccine. 2007;25(48):8133–39. doi:10.1016/j.vaccine.2007.09.039.
   PubMed Web of Science ®Google Scholar
• Neher RA, Bedford T, Daniels RS, Russell CA, Shraiman BI. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses. Proc Natl Acad Sci. 2016;113(12):E1701–E1709. doi:10.1073/pnas.1525578113.
   PubMed Web of Science ®Google Scholar
• Veljkovic V, Paessler S, Glisic S, Prljic J, Perovic VR, Veljkovic N, Scotch M. Evolution of 2014/15 H3N2 Influenza viruses circulating in US: consequences for vaccine effectiveness and possible new pandemic. Front Microbiol. 2015;6. doi:10.3389/fmicb.2015.01456.
   Web of Science ®Google Scholar
• Qiu J, Qiu T, Yang Y, Wu D, Cao Z. Incorporating structure context of HA protein to improve antigenicity calculation for influenza virus A/H3N2. Sci Rep. 2016;6:31156.
   Web of Science ®Google Scholar
• WHO. A description of the process of seasonal and H5N1 Influenza vaccine virus selection and development. World Health Organization; 2007.
   Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;1:1097–105.
   Google Scholar
• Simard PY, Steinkraus D, Platt JC. Best practices for convolutional neural networks applied to visual document analysis. ICDAR. Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.2003;958–62. https://www.semanticscholar.org/paper/Best-practices-for-convolutional-neural-networks-to-Simard-Steinkraus/5562a56da3a96dae82add7de705e2bd841eb00fc
   Google Scholar
• Toderici KG, Shetty S, Leung T, Sukthankar R, Fei-Fei L. Large-scale video classification with convolutional neural networks. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition; Columbus, OH; 2014. p. 1725–32. doi:10.1109/CVPR.2014.223.
   Google Scholar
• Ji S, Xu W, Yang M, Yu K. 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell. 2013;35(1):221–31. doi:10.1109/TPAMI.2012.59.
   PubMed Web of Science ®Google Scholar
• Kim Y. Convolutional neural networks for sentence classification. arXiv Preprint arXiv:1408.5882. 2014.
   Google Scholar
• Abdel-Hamid O, A-r M, Jiang H, Penn G. Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, (IEEE); New York (NY); 2012. p. 4277–80.
   Google Scholar
• Wang T, Wu DJ, Coates A, Ng AY. End-to-end text recognition with convolutional neural networks. Pattern Recognition (ICPR), 2012 21st International Conference on, (IEEE); Tsukuba (Japan); 2012. p. 3304–08.
   Google Scholar
• Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. International Conference on Medical Image Computing and Computer-assisted Intervention, Springer; Nagoya (Japan); 2013. p. 411–18.
   Google Scholar
• Wang H, Cruz-Roa A, Basavanhally A, Gilmore H, Shih N, Feldman M, Tomaszewski J, Gonzalez F, Madabhushi A. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J Med Imaging. 2014;1(3):034003–034003. doi:10.1117/1.JMI.1.3.034003.
   Google Scholar
• Cruz-Roa A, Basavanhally A, González F, Gilmore H, Feldman M, Ganesan S, Shih N, Tomaszewski J, Madabhushi A. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. SPIE medical imaging, (International Society for Optics and Photonics); San Diego (CA); 2014. p. 904103–904103.
   Google Scholar
• QiD, Chen H, Yu L, Zhao L, Qin J, Wang D, Ct Mok V, Shi L, Heng PA. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans Med Imaging. 2016;35(5):1182–95. doi:10.1109/TMI.2016.2528129.
   PubMed Web of Science ®Google Scholar
• Ghafoorian M, Karssemeijer N, Heskes T, van Uden IWM, de Leeuw FE, Marchiori E, van Ginneken B, Platel B. Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation. Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, (IEEE); Prague (Czech Republic); 2016. p. 1414–17.
   Google Scholar
• Sarraf S, Tofighi G. Classification of alzheimer’s disease using fMRI data and deep learning convolutional neural networks. arXiv Preprint arXiv:1603.08631. 2016.
   Google Scholar
• Quang D, Xie X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 2016;44(11):e107–e107. doi:10.1093/nar/gkw226.
   PubMed Web of Science ®Google Scholar
• Kim J. Detecting influenza outbreaks in United States by analyzing climatic heat maps using convolutional neural network. Internationa Conference Data Mining| DMIN'17, SBN: 1-60132-453-7; CSREA Press; Las Vegas (NV); 2017. p. 24–27.
   Google Scholar
• Zhong S, Bian L. Predicting Influenza dynamics using a deep learning approach. Int Conf GISci Short Pap Proc. 2016;1. doi:10.21433/B3113969C18V.
   PubMedGoogle Scholar
• Garro BA, Vázquez RA. Designing artificial neural networks using particle swarm optimization algorithms. Comput Intell Neurosci. 2015;2015:61. doi:10.1155/2015/369298.
   Web of Science ®Google Scholar
• Zoph B, Le QV. Neural architecture search with reinforcement learning. arXiv Preprint arXiv:1611.01578. 2016.
   Google Scholar
• Albelwi S, Mahmood A. A framework for designing the architectures of deep convolutional neural networks. Entropy. 2017;19:242.
   Web of Science ®Google Scholar
• Bergstra JS, Bardenet R, Bengio Y, Kégl B. Algorithms for hyper-parameter optimization. Adv Neural Inf Process Syst. 2011;24:2546–54.
   Google Scholar
• Karaboga D, Akay B, Ozturk C. Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. MDAI. 2007;7:318–19.
   Google Scholar
• Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res. 2012;13:281–305.
   Web of Science ®Google Scholar
• Snoek J, Larochelle H, Adams RP. Practical bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst. 2012;25:2951–59.
   Google Scholar
• Saxena S, Verbeek J. Convolutional neural fabrics. Adv Neural Inf Process Syst. 2016;29:4053–61.
   Google Scholar
• Mendoza H, Klein A, Feurer M, Springenberg JT, Hutter F. Towards automatically-tuned neural networks. Workshop Autom Mach Learn. 2016;64:58–65.
   Google Scholar
• Yao X. Evolving artificial neural networks. Proc IEEE. 1999;87(9):1423–47. doi:10.1109/5.784219.
   Web of Science ®Google Scholar
• Yu J, Xi L, Wang S. An improved particle swarm optimization for evolving feedforward artificial neural networks. Neural Process Lett. 2007;26(3):217–31. doi:10.1007/s11063-007-9053-x.
   Web of Science ®Google Scholar
• Young SR, Rose DC, Karnowski TP, Lim S-H, Patton RM. Optimizing deep learning hyper-parameters through an evolutionary algorithm. Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments, ACM; Austin (TX); 2015. p 4.
   Google Scholar
• Bedford T, Suchard MA, Lemey P, Dudas G, Gregory V, Hay AJ, McCauley JW, Russell CA, Smith DJ, Rambaut A. Integrating influenza antigenic dynamics with molecular evolution. Elife. 2014;3:e01914. doi:10.7554/eLife.01914.
   PubMed Web of Science ®Google Scholar
• Smith DJ, Lapedes AS, de Jong JC, Bestebroer TM, Rimmelzwaan GF, Osterhaus ADME, Fouchier RAM. Mapping the antigenic and genetic evolution of influenza virus. Science. 2004;305(5682):371–76. doi:10.1126/science.1097211.
   PubMed Web of Science ®Google Scholar
• Bao Y, Bolotov P, Dernovoy D, Kiryutin B, Zaslavsky L, Tatusova T, Ostell J, Lipman D. The influenza virus resource at the national center for biotechnology information. J Virol. 2008;82(2):596–601. doi:10.1128/JVI.02005-07.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Aevermann BD, Anderson TK, Burke DF, Dauphin G, Gu Z, He S, Kumar S, Larsen CN, Lee AJ. Influenza research database: an integrated bioinformatics resource for influenza virus research. Nucleic Acids Res. 2016;45(D1):D466–D474. doi:10.1093/nar/gkw857.
   PubMed Web of Science ®Google Scholar
• Elbe S, Buckland-Merrett G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Global Challenges. 2017;1(1):33–46. doi:10.1002/gch2.1018.
   PubMed Web of Science ®Google Scholar
• Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–97. doi:10.1093/nar/gkh340.
   PubMed Web of Science ®Google Scholar
• Ashkenazy H, Erez E, Martz E, Pupko T, Ben-Tal N. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res. 2010;38(suppl_2):W529–W533. doi:10.1093/nar/gkq399.
   PubMedGoogle Scholar
• Kawashima S, Pokarowski P, Pokarowska M, Kolinski A, Katayama T, Kanehisa M. AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. 2007;36(suppl_1):D202–D205. doi:10.1093/nar/gkm998.
   PubMedGoogle Scholar
• Chollet F. Keras. GitHub; 2015Chollet, F. (2015) keras, GitHub. https://github.com/fchollet/keras.
   Google Scholar
• Lee EK, Nakaya HI, Yuan F, Querec TD, Burel G, Pietz FH, Benecke BA, Pulendran B. Machine learning for predicting vaccine immunogenicity. Interfaces. 2016;46(5):368–90. doi:10.1287/inte.2016.0862.
   Web of Science ®Google Scholar
• Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, Pirani A, Gernert K, Deng J, Marzolf B, et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol. 2009;10(1):116. doi:10.1038/ni.1688.
   PubMed Web of Science ®Google Scholar
• Kennedy J, Mendes R. Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Trans Syst Man Cybern C Appl Rev. 2006;36(4):515–19. doi:10.1109/TSMCC.2006.875410.
   Web of Science ®Google Scholar
• Kennedy J. Particle swarm optimization. Encyclopedia of machine learning, Springer; 2011. p. 760–66. doi:10.1177/1753193411423140.
   Google Scholar
• Dennis J, Woods DJ. Optimization on microcomputers: the Nelder-Mead simplex algorithm. New Comput environ. 1987;11:116–22.
   Google Scholar
• Powell MJ. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J. 1964;7(2):155–62. doi:10.1093/comjnl/7.2.155.
   Web of Science ®Google Scholar
• Straeter TA. On the extension of the Davidson-Broyden class of rank one, quasi-newton minimization methods to an infinite dimensional hilbert space with applications to optimal control problems; NASA Technical Report Server, NASA; USA; 1971.
   Google Scholar
• Fletcher R. Practical methods of optimization. John Wiley & Sons; 2013. Wiley Online.
   Google Scholar
• Byrd RH, Lu P, Nocedal J, Zhu C. A limited memory algorithm for bound constrained optimization. SIAM J Sci Comput. 1995;16(5):1190–208. doi:10.1137/0916069.
   Web of Science ®Google Scholar
• Martens J. Deep learning via Hessian-free optimization. ICML. Proceedings of the 27th International Conference on Machine Learning; Haifa, Israel; 2010. p. 735–42.
   Google Scholar
• Conn AR, Scheinberg K, Toint PL. On the convergence of derivative-free methods for unconstrained optimization. In: Iserles A, Buhmann M, editors. Approximation theory and optimization: Tributes to M. J. D. Powell. Cambridge (UK): Cambridge University Press; 1997, p. 83–108.
   Google Scholar
• Nocedal J, Wright SJ. Sequential quadratic programming. Switzerland: Springer; 2006.
   Google Scholar
• Georgiev AG. Interpretable numerical descriptors of amino acid space. J Comput Biol. 2009;16(5):703–23. doi:10.1089/cmb.2008.0173.
   PubMed Web of Science ®Google Scholar
• van Westen GJ, Swier RF, Cortes-Ciriano I, Wegner JK, Overington JP, IJzerman AP, van Vlijmen HW, Bender A. Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets. J Cheminform. 2013;5(1):42. doi:10.1186/1758-2946-5-42.
   PubMedGoogle Scholar
• Control CfD & Prevention. Preliminary assessment of the effectiveness of the 2003-04 inactivated influenza vaccine–Colorado, December 2003. MMWR Morb Mortal Wkly Rep. 2004;53(1):8.
   PubMedGoogle Scholar
• Belongia EA, Kieke B, Donahue J, Greenlee R, Balish A, Foust A, Lindstrom S, Shay D. Effectiveness of inactivated influenza vaccines varied substantially with antigenic match from the 2004–2005 season to the 2006–2007 season. J Infect Dis. 2009;199(2):159–67. doi:10.1086/597213.
   PubMed Web of Science ®Google Scholar
• Saito R, Suzuki H, Oshitani H, Sakai T, Seki N, Tanabe N. The effectiveness of influenza vaccine against influenza A (H3N2) virus infections in nursing homes in Niigata, Japan, during the 1998-1999 and 1999-2000 seasons. Infect Control Hosp Epidemiol. 2002;23(2):82–86. doi:10.1086/502011.
   PubMed Web of Science ®Google Scholar
• Control CfD & Prevention. Interim within-season estimate of the effectiveness of trivalent inactivated influenza vaccine–Marshfield, Wisconsin, 2007-08 influenza season. MMWR Morb Mortal Wkly Rep. 2008;57(15):393.
   PubMedGoogle Scholar
• Lee M-S, Chen JS-E. Predicting antigenic variants of influenza A/H3N2 viruses. Emerg Infect Dis. 2004;10(8):1385. doi:10.3201/eid1008.040107.
   PubMed Web of Science ®Google Scholar
• CDC. 2000-2001 Influenza season summary. Atlanta (GA): Centers for Disease Control and Prevention; 2001.
   Google Scholar