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2017 | OriginalPaper | Buchkapitel

A Review of Deep Learning Architectures and Their Application

verfasst von : Jalilah Arijah Mohd Kamarudin, Afnizanfaizal Abdullah, Roselina Sallehuddin

Erschienen in: Modeling, Design and Simulation of Systems

Verlag: Springer Singapore

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Abstract

Deep Learning is a new era of machine learning research that are making major advances in solving problem with powerful computational models. Currently, this new machine learning method is widely used in object detection, visual object and speech recognition and also for making prediction of regulatory genomic and cellular imaging. Here, we review the methodology and applications of deep learning architectures including deep neural network, convolutional neural network and recurrent neural network. Next, we review several existing prediction tools in genomic sequences analysis that use deep learning architectures. In addition, we discuss the future research directions of deep learning.

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Vorheriges Kapitel Xylitol Production of E. coli Using Deep Neural Network and Firefly Algorithm

Nächstes Kapitel The Enhancement of Evolving Spiking Neural Network with Dynamic Population Particle Swarm Optimization

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef

Larranaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J.A., Armañanzas, R., Santafé, G., Pérez, A., Robles, V.: Machine learning in bioinformatics. Brief. Bioinform. 7(1), 86–112 (2006)CrossRef

Abdullah, A., Deris, S., Hashim, S.Z.M., Jamil, H.M.: Graph partitioning method for functional module detections of protein interaction network. In: International Conference on Computer Technology and Development, ICCTD 2009, vol. 1, pp. 230–234. IEEE, November 2009

Ismail, M.A., Deris, S., Mohamad, M.S., Abdullah, A.: A Newton cooperative genetic algorithm method for in Silico optimization of metabolic pathway production. PLoS ONE 10(5), e0126199 (2015)CrossRef

Hayashi, N., Matsumae, M., Yatsushiro, S., Hirayama, A., Abdullah, A., Kuroda, K.: Quantitative analysis of cerebrospinal fluid pressure gradients in healthy volunteers and patients with normal pressure hydrocephalus. Neurol. Med. Chir. 55(8), 657–662 (2015)CrossRef

Abdullah, A., Hirayama, A., Yatsushiro, S., Matsumae, M., Kuroda, K.: Cerebrospinal fluid image segmentation using spatial fuzzy clustering method with improved evolutionary expectation maximization. In: 2013 35th Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3359–3362. IEEE, July 2013

Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)CrossRef

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in Neural Information Processing Systems, pp. 1799–1807 (2014)

10.

Liu, N., Han, J., Zhang, D., Wen, S., Liu, T.: Predicting eye fixations using convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 362–370 (2015)

11.

Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., Kingsbury, B.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)CrossRef

12.

Sainath, T.N., Mohamed A.-R., Kingsbury, B., et al.: Deep convolutional neural networks for LVCSR. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), p. 8614–8. IEEE (2013)

13.

Min, S., Lee, B., Yoon, S. Deep learning in bioinformatics. Brief. Bioinform. bbw068 (2016)

14.

Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., Fidler, S.: Skip-thought vectors. In: Advances in Neural Information Processing Systems, pp. 3294–3302 (2015)

15.

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

16.

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

17.

Zhang, S., Zhou, J., Hu, H., Gong, H., Chen, L., Cheng, C., Zeng, J.: A deep learning framework for modeling structural features of RNA-binding protein targets. Nucl. Acids Res. 44(4), e32–e32 (2016)CrossRef

18.

Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40763-5_72 CrossRef

19.

Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J.: Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol. 33(8), 831–838 (2015)CrossRef

20.

Zhou, J., Troyanskaya, O.G.: Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12(10), 931–934 (2015)CrossRef

21.

Hua, K.L., Hsu, C.H., Hidayati, S.C., Cheng, W.H., Chen, Y.J.: Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets Ther. 8, 2015–2022 (2014)

22.

Roth, H.R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Summers, R.M.: Improving computer-aided detection using <? Pub _newline ?> convolutional neural networks and random view aggregation. IEEE Trans. Med. Imaging 35(5), 1170–1181 (2016)CrossRef

23.

Roth, H.R., Yao, J., Lu, L., Stieger, J., Burns, J.E., Summers, R.M.: Detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications. In: Yao, J., Glocker, B., Klinder, T., Li, S. (eds.) Recent Advances in Computational Methods and Clinical Applications for Spine Imaging. LNCVB, vol. 20, pp. 3–12. Springer, Cham (2013). doi:10.1007/978-3-319-14148-0_1 CrossRef

24.

Baldi, P., Pollastri, G., Andersen, C.A., Brunak, S.: Matching protein b-sheet partners by feedforward and recurrent neural networks. In: ISMB, pp. 25–36 (2000)

25.

Ogawa, S., Lee, T.M., Kay, A.R., Tank, D.W.: Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl. Acad. Sci. 87(24), 9868–9872 (1990)CrossRef

26.

Hsieh, J.: Computed Tomography: Principles, Design, Artifacts, and Recent Advances, vol. 114. SPIE Press, Bellingham (2003)

27.

McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)MathSciNetCrossRefMATH

28.

Farley, B.W.A.C., Clark, W.: Simulation of self-organizing systems by digital computer. Trans. IRE Prof. Group Inf. Theory 4(4), 76–84 (1954)MathSciNetCrossRef

29.

Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386 (1958)CrossRef

30.

Svozil, D., Kvasnicka, V., Pospichal, J.: Introduction to multi-layer feed-forward neural networks. Chemometr. Intell. Lab. Syst. 39(1), 43–62 (1997)CrossRef

31.

Heffernan, R., Paliwal, K., Lyons, J., Dehzangi, A., Sharma, A., Wang, J., Sattar, A., Yang, Y., Zhou, Y.: Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Sci. Rep. 5, 11476 (2015)CrossRef

32.

Chen, Y., Li, Y., Narayan, R., Subramanian, A., Xie, X.: Gene expression inference with deep learning. Bioinformatics 32(12), 1832–1839 (2016)CrossRef

33.

Fakoor, R., Ladhak, F., Nazi, A., Huber, M.: Using deep learning to enhance cancer diagnosis and classification. In: Proceedings of International Conference on Machine Learning (2013)

34.

van Gerven, M.A., de Lange, F.P., Heskes, T.: Neural decoding with hierarchical generative models. Neural Comput. 22(12), 3127–3142 (2010)CrossRefMATH

35.

Koyamada, S., Shikauchi, Y., Nakae, K., Koyama, M., Ishii, S.: Deep learning of fMRI big data: a novel approach to subject-transfer decoding. arXiv preprint arXiv:1502.00093 (2010)

36.

Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., Wang, T.: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans. Med. Imaging 35(1), 109–118 (2016)CrossRef

37.

Xu, J., Xiang, L., Liu, Q., Gilmore, H., Wu, J., Tang, J., Madabhushi, A.: Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans. Med. Imaging 35(1), 119–130 (2016)CrossRef

38.

Plis, S.M., Hjelm, D.R., Salakhutdinov, R., Calhoun, V.D.: Deep learning for neuroimaging: a validation study. arXiv preprint arXiv:1312.5847 (2013)

39.

Hubel, D.H., Wiesel, T.N.: Shape and arrangement of columns in cat’s striate cortex. J. Physiol. 165(3), 559 (1963)CrossRef

40.

Hubel, D.H., Wiesel, T.N.: The period of susceptibility to the physiological effects of unilateral eye closure in kittens. J. Physiol. 206(2), 419 (1970)CrossRef

41.

Angermueller, C., Pärnamaa, T., Parts, L., Stegle, O.: Deep learning for computational biology. Mol. Syst. Biol. 12(7), 878 (2016)CrossRef

42.

Kelley, D.R., Snoek, J., Rinn, J.: Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. (2016). doi:10.1101/gr.200535.115

43.

Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

44.

Liwicki, M., Graves, A., Bunke, H., Schmidhuber, J.: A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. In: Proceedings of 9th International Conference on Document Analysis and Recognition, vol. 1, pp. 367–371 (2007)

45.

Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)CrossRef

46.

Wulsin, D.F., Gupta, J.R., Mani, R., Blanco, J.A., Litt, B.: Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement. J. Neural Eng. 8(3), 036015 (2011)CrossRef

47.

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

48.

Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of 25th International Conference on Machine Learning, pp. 1096–1103. ACM (2008)

49.

Quang, D., Xie, X.: DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucl. Acids Res. 44(11), e107–e107 (2016)CrossRef

50.

Tripathi, R., Patel, S., Kumari, V., Chakraborty, P., Varadwaj, P.K.: DeepLNC, a long non-coding RNA prediction tool using deep neural network. Netw. Model. Anal. Health Inform. Bioinform. 5(1), 1–14 (2016)CrossRef

51.

Lanchantin, J., Singh, R., Lin, Z., Qi, Y.: Deep motif: visualizing genomic sequence classifications. arXiv preprint arXiv:1605.01133 (2016)

52.

Leung, M.K., Delong, A., Alipanahi, B., Frey, B.J.: Machine learning in genomic medicine: a review of computational problems and data sets. Proc. IEEE 104(1), 176–197 (2016)CrossRef

53.

Abdullah, A., Deris, S., Mohamad, M.S., Anwar, S.: An improved swarm optimization for parameter estimation and biological model selection. PLoS ONE 8(4), e61258 (2013)CrossRef

54.

Abdullah, A., Deris, S., Hashim, S.Z.M., Mohamad, M.S., Arjunan, S.N.V.: An improved local best searching in particle swarm optimization using differential evolution. In: 2011 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 115–120. IEEE, December 2011

Titel: A Review of Deep Learning Architectures and Their Application
verfasst von: Jalilah Arijah Mohd Kamarudin
Afnizanfaizal Abdullah
Roselina Sallehuddin
Verlag: Springer Singapore
Buch: Modeling, Design and Simulation of Systems
Print ISBN: 978-981-10-6501-9

Electronic ISBN: 978-981-10-6502-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-981-10-6502-6_7

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