Secondary structure Prediction of Proteins using Deep Learning Network Approach- a Review Study

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-38 Number-2
Year of Publication : 2016
Authors : Sachin Poothia
  10.14445/22312803/IJCTT-V38P113

MLA

Sachin Poothia "Secondary structure Prediction of Proteins using Deep Learning Network Approach- a Review Study". International Journal of Computer Trends and Technology (IJCTT) V38(2):67-70, August 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
In the recent years Machine learning techniques based on deep learning networks have shown a great promise in research community. They have been quite successful in solving problems using unsupervised learning techniques. One of the most successful deep learning methods involves artificial neural networks. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. DNNs have recently shown better results than conventional methods in some areas as they are capable of learning intermediate representations, with each layer of the network learning a slightly more abstract representation than the previous layer [16, 17]. This paper is aimed to review the recent work based on deep learning networks with an aim to understand its impact for notable improvements in the field of secondary structure prediction of proteins. The objective of this review is to motivate and facilitate deep learning studies for secondary structure prediction of proteins using deep learning networks.

References
[1] Matt Spencer, Jesse Eickholt, and Jianlin Cheng; A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction IEEE/ACM Trans Comput Biol Bioinform. 2015 Jan-Feb; 12(1): 103–112. Published online 2014 Aug 7. doi: 10.1109/TCBB.2014.2343960
[2] Floudas C, Fung H, McAllister S, Mönnigmann M, Rajgaria R. Advances in protein structure prediction and de novo protein design: A review. Chemical Engineering Science.2006;61:966–988.
[3] Kopp J, Schwede T. Automated protein structure homology modeling: a progress report. Pharmacogenomics. 2004;5:405–416. [PubMed]
[4] Jones DT, Taylor WR, Thornton JM. A new approach to protein fold recognition. Nature.1992;358:86–9. [PubMed]
[5] Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural computation. 2006;18:1527– 1554. [PubMed]
[6] Glauner, P. (2015). Deep Convolutional Neural Networks for Smile Recognition (MSc Thesis).Imperial College London, Department of Computing. arXiv:1508.06535.
[7] Song, H.A.; Lee, S. Y. (2013). "Hierarchical Representation Using NMF". Neural Information Processing. Lectures Notes in Computer Sciences 8226. Springer Berlin Heidelberg. pp. 466–473.doi:10.1007/978-3-642-42054- 2_58. ISBN 978-3-642-42053-5.
[8] J. Schmidhuber., "Learning complex, extended sequences using the principle of history compression," Neural Computation, 4, pp. 234–242, 1992.
[9] J. Schmidhuber., "My First Deep Learning System of 1991 + Deep Learning Timeline 1962–2013."
[10] Keynote talk: Recent Developments in Deep Neural Networks. ICASSP, 2013 (by Geoff Hinton).
[11] http://research.microsoft.com/apps/pubs/default.aspx?id=18 9004
[12] Magnan, C. N. & Baldi, P. SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics 30, 2592-2597 (2014).
[13] Peng, J., Bo, L. & Xu, J. Conditional neural fields. Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2009, 1419-1427 (2009)
[14] Arel I et al. Deep Machine Learning- A New Frontier in Artificial Intelligence Research. IEEE Computational Intelligence 2010; 13-18.
[15] DiLena P et al. Deep Architecture for Protein Contact Map Prediction. Bioinformatics 2012; 28(19): 2449-2457.
[16] LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521: 436-444.
[17] Bengio Y, Courville A, Vincent P (2013) Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35: 1798-1828.
[18] Jian Zhou and Olga G. Troyanskaya. Deep supervised and convolutional generative stochastic network for protein secondary structure prediction. Journal of Machine Learning Research: W&CP, 32(1):754-762, 2014.
[19] James Lyons, Abdollah Dehzangi, Rhys Heffernan, Alok Sharma, Kuldip Paliwal, Abdul Sattar, Yaoqi Zhou, and Yuedong Yang. Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse autoencoder deep neural network. Journal of Computational Chemistry, 35(28):2040-2046, 2014
[20] Yanjun Qi, Merja Oja, Jason Weston, and William Stafford Noble. A unified multitask architecture for predicting local protein properties. PLoS ONE, 7(3):e32235, 2012.
[21] Pietro Di Lena, Ken Nagata, and Pierre Baldi. Deep spatiotemporal architectures and learning for protein structure prediction. Advances in Neural Information Processing Systems (NIPS) 25, pages 521-529, Lake Tahoe, Nevada, December 3 – 6, 2012.
[22] Pietro Di Lena, Ken Nagata, and Pierre Baldi. Deep architectures for protein contact map prediction. Bioinformatics, 28(19):2449-2457, 2012.

Keywords
The objective of this review is to motivate and facilitate deep learning studies for secondary structure prediction of proteins using deep learning networks.