Taxonomic classification for living organisms using convolutional neural networks

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2017-11-17
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Genes, Volume 8, issue 11
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.
Description
Keywords
Convolutional neural networks, DNA, Encoding, Genes, Taxonomic classification
Other note
Citation
Khawaldeh , S , Pervaiz , U , Elsharnoby , M , Alchalabi , A E & Al-Zubi , N 2017 , ' Taxonomic classification for living organisms using convolutional neural networks ' , Genes , vol. 8 , no. 11 , 326 . https://doi.org/10.3390/genes8110326