Abstract
Deep Neural Networks show their promise over traditional neural network on DNA genomic analysis. However, due to the uncertainty of DNA sequence data, it performs differently in various encoding schemes. In this article we focus on the comparison of different schemes on various auto-encoder algorithms in DNA annotation and analyze their impacts on deep learning. We also aim to find the best encoding schemes used on deep auto-encoder algorithms for DNA annotation.
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
References
Wu, X., Cai, Z., Wan, X.-F., Hoang, T., Goebel, R., Lin, G.: Nucleotide composition string selection in HIV-1 subtyping using whole genomes. Bioinformatics 23(14), 1744–1752 (2007)
Yu, N., Guo, X., Gu, F., Pan, Y.: DNA AS X: an information-coding-based model to improve the sensitivity in comparative gene analysis. In: Harrison, R., Li, Y., Măndoiu, I. (eds.) ISBRA 2015. LNCS, vol. 9096, pp. 366–377. Springer, Cham (2015). doi:10.1007/978-3-319-19048-8_31
Wu, J., Wan, X.F., Xu, L., Lin, G., Cai, Z., Goebel, R.: Identifying a few foot-and-mouth disease virus signature nucleotide strings for computational genotyping. BMC Bioinform. 9, 279 (2008)
Nair, A., Sreenadhan, S.: A coding measure scheme employing electron-ion interaction pseudopotential (EIIP). Bioinformation 1(6), 197–202 (2006)
Arniker, S.B., Kwan, H.K., Law, N.F., Lun, D.P.K.: DNA numerical representation and neural network based human promoter prediction system. In: 2011 Annual IEEE India Conference, pp. 1–4, December 2011
Kauer, G., Blöcker, H.: Applying signal theory to the analysis of biomolecules. Bioinformatics 19(16), 2016–2021 (2003)
Jabbari, K., Bernardi, G.: Cytosine methylation and CpG, TpG (CpA) and TpA frequencies. Gene 26(333), 143–149 (2004)
Rosen, G.L.: Signal processing for bibiological-inspired gradient source localization and DNA sequence analysis. Ph.D. dissertation, Georgia Institute of Technology, School of Electrical and Computer Engineering, August 2006
Hinton, G., Dayan, P., Frey, B., Neal, R.: The “wake-sleep” algorithm for unsupervised neural networks. Science 268(5214), 1158–1161 (1995)
Hintonemail, G.E.: Learning multiple layers of representation. Trends Cogn. Sci. 11(10), 428–434 (2007)
Deng, L., Hinton, G., Kingsbury, B.: New types of deep neural network learning for speech recognition and related applications: an overview. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8599–8603, May 2013
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Di Lena, P., Nagata, K., Baldi, P.: Deep architectures for protein contact map prediction. Bioinformatics 28(19), 2449–2457 (2012)
Eickholt, J., Cheng, J.: Predicting protein residueresidue contacts using deep networks and boosting. Bioinformatics 28(23), 3066–3072 (2012)
Leung, M.K.K., Xiong, H.Y., Lee, L.J., Frey, B.J.: Deep learning of the tissue-regulated splicing code. Bioinformatics 30(12), i121–i129 (2014)
Reese, M., Eeckman, F., Kulp, D., Haussler, D.: Improved splice site detection in genie. J. Comput. Biol. 4(3), 311–323 (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yu, N., Yu, Z., Gu, F., Pan, Y. (2017). Evaluating the Impact of Encoding Schemes on Deep Auto-Encoders for DNA Annotation. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-59575-7_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59574-0
Online ISBN: 978-3-319-59575-7
eBook Packages: Computer ScienceComputer Science (R0)