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Genes 2017, 8(11), 326; doi:10.3390/genes8110326

Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

1
Erasmus+ Joint Master Program in Medical Imaging and Applications, University of Burgundy, 21000 Dijon, France
2
Erasmus+ Joint Master Program in Medical Imaging and Applications, UNICLAM, 03043 Cassino FR, Italy
3
Erasmus+ Joint Master Program in Medical Imaging and Applications, University of Girona, 17004 Girona, Spain
4
Graduate School of Natural and Applied Sciences, Istanbul Sehir University, 34865 Kartal/İstanbul, Turkey
5
Department of Computer Engineering, Al-Balqa’ Applied University, 19117 Al-Salt, Jordan
6
Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
*
Author to whom correspondence should be addressed.
Received: 11 September 2017 / Revised: 5 November 2017 / Accepted: 14 November 2017 / Published: 17 November 2017
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Abstract

Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential for using it in many other applications in genome analysis. View Full-Text
Keywords: DNA; genes; taxonomic classification; convolutional neural networks; encoding DNA; genes; taxonomic classification; convolutional neural networks; encoding
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Khawaldeh, S.; Pervaiz, U.; Elsharnoby, M.; Alchalabi, A.E.; Al-Zubi, N. Taxonomic Classification for Living Organisms Using Convolutional Neural Networks. Genes 2017, 8, 326.

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