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Lee, H., Grosse, R., Ranganath, R. and Ng, Y.A. (2009) Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical. Proceeding of the 26th Annual International Conference on Machine Learning, Montreal, 14-18 June 2009, 609-616.
http://dx.doi.org/10.1145/1553374.1553453

has been cited by the following article:

• TITLE: DNA Sequence Classification by Convolutional Neural Network

  AUTHORS: Ngoc Giang Nguyen, Vu Anh Tran, Duc Luu Ngo, Dau Phan, Favorisen Rosyking Lumbanraja, Mohammad Reza Faisal, Bahriddin Abapihi, Mamoru Kubo, Kenji Satou

  KEYWORDS: DNA Sequence Classification, Deep Learning, Convolutional Neural Network

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.9 No.5, April 27, 2016

  ABSTRACT: In recent years, a deep learning model called convolutional neural network with an ability of extracting features of high-level abstraction from minimum preprocessing data has been widely used. In this research, we proposed a new approach in classifying DNA sequences using the convolutional neural network while considering these sequences as text data. We used one-hot vectors to represent sequences as input to the model; therefore, it conserves the essential position information of each nucleotide in sequences. Using 12 DNA sequence datasets, we evaluated our proposed model and achieved significant improvements in all of these datasets. This result has shown a potential of using convolutional neural network for DNA sequence to solve other sequence problems in bioinformatics.