The prediction of virus mutation using neural networks and rough set techniques.
The prediction of virus mutation using neural networks and rough set techniques.
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DOI：
10.1186/s13637-016-0042-0
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Mostafa A
中科院分区：
文献类型：
--
作者：
Salama MA;Hassanien AE;Mostafa A
病毒进化仍然是抗病毒治疗有效性的主要障碍。预测这种演变的能力将有助于及早发现耐药菌株，并有可能促进更有效的抗病毒治疗的设计。在基因组研究中已经使用了各种工具来实现这一目标。这些工具之一是机器学习，它促进了结构-活性关系的研究、二级和三级结构进化预测以及序列错误校正。这项工作提出了一种新的机器学习技术，用于预测出现在初级RNA序列结构比对上的可能的点突变。它预测了RNA序列中每个核苷酸的基因型，并证明了RNA序列中的一个核苷酸会根据序列中的其他核苷酸发生变化。利用神经网络技术对新菌株进行预测，然后引入基于粗糙集理论的算法来提取这些点突变模式。该算法被应用于新城疫病毒时间序列的多个RNA分离株的比对。在验证这些技术时使用了来自两个来源的两个不同的数据集。结果表明，该技术预测新一代核苷酸的准确率高达75%。突变规则被可视化，用于分析同一RNA序列中不同核苷酸之间的相关性。
Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.