Khawaldeh Saed, Pervaiz Usama, Elsharnoby Mohammed, Alchalabi Alaa Eddin, Al-Zubi Nayel
Erasmus+ Joint Master Program in Medical Imaging and Applications, University of Burgundy, 21000 Dijon, France.
Erasmus+ Joint Master Program in Medical Imaging and Applications, UNICLAM, 03043 Cassino FR, Italy.
Genes (Basel). 2017 Nov 17;8(11):326. doi: 10.3390/genes8110326.
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.
分类学分类有广泛的应用，比如更多地了解进化历史。与自然界所蕴藏的生物体估计数量相比，人类对它们具体所属的特定类别并没有透彻的理解。生物体的分类可以通过许多机器学习技术来完成。然而，在本研究中，这是使用卷积神经网络来进行的。此外，一种DNA编码技术被纳入算法以提高性能并避免错误分类。所提出的算法在准确性和灵敏度方面优于现有算法，这表明它在基因组分析的许多其他应用中具有很高的应用潜力。
