基于神经网络和粗糙集技术的病毒变异预测。

EURASIP journal on bioinformatics & systems biology Pub Date : 2016-05-13 eCollection Date: 2016-12-01 DOI:10.1186/s13637-016-0042-0
Mostafa A Salama, Aboul Ella Hassanien, Ahmad Mostafa
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引用次数: 28

摘要

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早期发现耐药菌株,并可能促进设计更有效的抗病毒治疗方法。为了实现这一目标,基因组研究中使用了各种工具。其中一种工具是机器学习,它有助于研究结构-活动关系,二级和三级结构演化预测以及序列误差校正。这项工作提出了一种新的机器学习技术,用于预测初级RNA序列结构对齐上可能出现的点突变。它预测了RNA序列中每个核苷酸的基因型,并证明了RNA序列中的核苷酸会随着序列中其他核苷酸的变化而变化。首先利用神经网络技术预测新菌株,然后引入基于粗糙集理论的点突变模式提取算法。该算法应用于许多排列的RNA分离物的时间序列物种的纽卡斯尔病毒。在验证这些技术时,使用了来自两个来源的两个不同数据集。结果表明,该技术预测新一代核苷酸的准确率高达75%。突变规则可视化分析在同一RNA序列中不同核苷酸之间的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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The prediction of virus mutation using neural networks and rough set techniques.

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.

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