A robust deep learning mechanism augmented with cellular automata for DNA computing

Sree, Pokkuluri Kiran; Devi, N. S. S. S. N. Usha; Sudheer, Manjunath

doi:10.1109/icpcsi.2017.8391921

Cited by 1 publication

(2 citation statements)

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“…The convolution neural network and recurrent neural network have been adopted in gene expression analysis, enhancer and regulatory region prediction, and methylation prediction. Sree et al [15] proposed a novel deep learning component for quicker DNA processing.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A novel genome analysis method with the entropy-based numerical technique using pretrained convolutional neural networks

Daş¹,

Toraman²,

Türkoğlu³

2020

Turk J Elec Eng & Comp Sci

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The identification of DNA sequences as exon and intron is a common problem in genome analysis. The methods used for feature extraction and mapping techniques for the digitization of sequences affect directly the solution of this problem. The existing mapping techniques are not enough to detect coding and noncoding regions in some genomes because the digital representation of each base in a DNA sequence with an integer does not fully reflect the structure of an original DNA sequence. In the entropy-based mapping technique, we could overcome this problem because the technique deepens distinction rates of exon regions, and better reflects the complexity of DNA sequences. Moreover, in the literature, features are extracted by using various statistical techniques. The statistical features to be extracted are chosen by a system designer's experience. The other proposed approach in this study is to carry out the feature extraction using the transfer learning method. Transfer learning and feature extraction are performed automatically by convolutional neural network models as independent of the data set. In this study, we propose a new method to classify DNA sequences as exon and intron using two approaches. In the first approach, the entropy-based numerical technique was used for the numerical representation of DNA sequences. In the second approach, transfer learning was used to extract features. Then, the obtained features were classified by support vector machine and k -nearest neighbors algorithm. As a result of the classification, accurate performance with 97.8% was achieved. The performance of the current method was compared with the other numerical mapping techniques and feature extraction methods. The results showed that the developed method was much more successful than other methods.

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Section: Related Workmentioning

confidence: 99%

“…In this study the results showed that the deep learning component has increased the prediction accuracy with 12.3%. The authors in [15] developed a novel unsupervised classifier that used hybrid cellular automata with deep learning to address some problems in bioinformatics. This classifier predicts the gene with 98.7% accuracy.…”

Section: Related Workmentioning

confidence: 99%