Taxonomic classification for living organisms using convolutional neural networks

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)
358 Downloads (Pure)


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.

Original languageEnglish
Article number326
Issue number11
Publication statusPublished - 17 Nov 2017
MoE publication typeA1 Journal article-refereed


  • Convolutional neural networks
  • DNA
  • Encoding
  • Genes
  • Taxonomic classification


Dive into the research topics of 'Taxonomic classification for living organisms using convolutional neural networks'. Together they form a unique fingerprint.

Cite this