Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

Khawaldeh, Saed
Pervaiz, Usama
Elsharnoby, Mohammed
Alchalabi, Alaa Eddin
Al-Zubi, Nayel
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 ​
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