ISSN: 2375-3811
International Journal of Biological Sciences and Applications  
Manuscript Information
 
 
Progress on Deep Learning in Bioinformatics
International Journal of Biological Sciences and Applications
Vol.4 , No. 6, Publication Date: Oct. 17, 2017, Page: 82-86
1693 Views Since October 17, 2017, 834 Downloads Since Oct. 17, 2017
 
 
Authors
 
[1]    

Yanqiu Tong, Department of Humanity, Chongqing Jiaotong University, Chongqing, China.

[2]    

Yang Song, Department of Device, Chongqing Medical University, Chongqing, China.

 
Abstract
 

With the development of next generation sequencing, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. The application of deep learning in bioinformatics has gained more attention in both academia and industry field. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. The compared research method was used to describe the application of deep learning in bioinformatics from many academic papers. In this paper, three types of deep learning algorithms (deep neural networks, convolutional neural networks, recurrent neural networks) have been introduced in bioinformatics, especially in the domain of omics. The review of this paper can provide valuable insight for researchers to utilize deep learning models in the future of bioinformatics studies.


Keywords
 

Machine Learning, Deep Learning, Bioinformatics


Reference
 
[01]    

Manyika J, Chui M, Brown B et al. Big data: The next frontier for innovation, competition, and productivity 2011.

[02]    

Larrañaga P, Calvo B, Santana R et al. Machine learning in bioinformatics. Briefings in bioinformatics 2006; 7 (1): 86-112.

[03]    

LeCun Y, Ranzato M. Deep learning tutorial. In: Tutorials in International Conference on Machine Learning (ICML’13). 2013. Citeseer.

[04]    

Min S, Lee B, Yoon S. Deep learning in bioinformatics [J]. Briefings in Bioinformatics, 2016.

[05]    

Nair V, Hinton G. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10). 2010. p. 807-14.

[06]    

Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex. The Journal of physiology 1968; 195 (1): 215-43.

[07]    

Schuster M, Paliwal KK. Bidirectional recurrent neural networks. Signal Processing, IEEE Transactions on 1997; 45 (11): 2673-81.

[08]    

Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. Neural Networks, IEEE Transactions on 1994; 5 (2): 157-66.

[09]    

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation 1997; 9 (8): 1735-80.

[10]    

Gers FA, Schmidhuber J, Cummins F. Learning to forget: Continual prediction with LSTM. Neural Computation 2000; 12 (10): 2451-71.

[11]    

Kiros R, Zhu Y, Salakhutdinov RR et al. Skip-thought vectors. In: Advances in neural information processing systems. 2015. p. 3276-84.

[12]    

Li J, Luong M-T, Jurafsky D. A hierarchical neural autoencoder for paragraphs and documents. arXiv preprint arXiv:1506.01057 2015.

[13]    

Luong M-T, Pham H, Manning CD. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 2015.

[14]    

Cho K, Van Merriënboer B, Gulcehre C et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 2014.

[15]    

Jones DT. Protein secondary structure prediction based on position-specific scoring matrices. Journal of molecular biology 1999; 292 (2): 195-202.

[16]    

Ponomarenko JV, Ponomarenko MP, Frolov AS et al. Conformational and physicochemical DNA features specific for transcription factor binding sites. Bioinformatics 1999; 15 (7): 654-68.

[17]    

Branden CI. Introduction to protein structure. Garland Science, 1999.

[18]    

Richardson JS. The anatomy and taxonomy of protein structure. Advances in protein chemistry 1981; 34: 167-339.

[19]    

Lena PD, Nagata K, Baldi P. Deep architectures for protein contact map prediction. Bioinformatics 2012; 28 (19): 2449-57.

[20]    

Baldi P, Pollastri G. The principled design of large-scale recursive neural network architectures-dag-rnns and the protein structure prediction problem. The Journal of Machine Learning Research 2003; 4: 575-602.

[21]    

Asgari E, Mofrad MR. Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics. PloS one 2015; 10 (11): e0141287.

[22]    

Hochreiter S, Heusel M, Obermayer K. Fast model-based protein homology detection without alignment. Bioinformatics 2007; 23 (14): 1728-36.

[23]    

Sønderby SK, Sønderby CK, Nielsen H et al. Convolutional LSTM Networks for Subcellular Localization of Proteins. arXiv preprint arXiv:1503.01919 2015.

[24]    

Nilsen TW, Graveley BR. Expansion of the eukaryotic proteome by alternative splicing. Nature 2010; 463 (7280): 457-63.

[25]    

Park Y, Kellis M. Deep learning for regulatory genomics. Nature biotechnology 2015; 33 (8): 825-6.

[26]    

Yao L, Torabi A, Cho K et al. Describing videos by exploiting temporal structure. In: Proceedings of the IEEE International Conference on Computer Vision. 2015. p. 4507-15.

[27]    

Noh H, Seo PH, Han B. Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction. arXiv preprint arXiv:1511.05756 2015.

[28]    

Graves A, Wayne G, Danihelka I. Neural turing machines. arXiv preprint arXiv:1410.5401 2014.

[29]    

Weston J, Chopra S, Bordes A. Memory networks. arXiv preprint arXiv:1410.3916 2014.

[30]    

Szegedy C, Zaremba W, Sutskever I et al. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 2013.

[31]    

Goodfellow I, Pouget-Abadie J, Mirza M et al. Generative adversarial nets. In: Advances in neural information processing systems. 2014. p. 2672-80.

[32]    

Lee T, Choi M, Yoon S. Manifold Regularized Deep Neural Networks using Adversarial Examples. arXiv preprint arXiv:1511.06381 2015.

[33]    

Rasmus A, Berglund M, Honkala M et al. Semi-Supervised Learning with Ladder Networks. In: Advances in neural information processing systems. 2015. p. 3532-40.

[34]    

Arel I. Deep Reinforcement Learning as Foundation for Artificial General Intelligence. Theoretical Foundations of Artificial General Intelligence. Springer, 2012, 89-102.





 
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