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使用基于注意力的神经机器翻译来预测SARS-CoV-2基因组中的突变

Darrak Moin Quddusi¹, Sandesh Athni Hiremath¹, Naim Bajcinca¹

¹Chair of Mechatronics in the Faculty of Mechanical and Process Engineering, Rheinland-Pfalz Technical University of Kaiserslautern-Landau, Kaiserslautern 67663, Germany.

Mathematical biosciences and engineering : MBE

|June 14, 2024

概括

使用神经机器翻译预测严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 突变有助于预测新的变种. 这项研究开发了基于RNN的NMT模型来预测病毒演变,有助于对未来传染性菌株的准备.

科学领域:

病毒学病毒学
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

严重急性呼吸道综合征冠状病毒2 (SARS-CoV-2) 呈现出快速演变和频繁突变,使制工作复杂化.
病毒基因组的变化可能会导致对当前疫苗和抗病毒疗法的耐药性增加.
积极预测病毒突变对于开发有效的对策和减轻流行病影响至关重要.

研究的目的:

提出和评估用于预测SARS-CoV-2突变的新型神经机器翻译 (NMT) 架构.
研究复发性神经网络 (RNN) 的应用,用于预测特定SARS-CoV-2非结构蛋白 (NSP) 的病毒演变.

主要方法:

开发使用循环神经网络 (RNN) 的多个NMT架构.
创建和预处理使用k-means集群和最近邻居进行NMT模型训练的序列对.
探索延长生物序列的NMT培训方法.
评估和对拟议的NMT模型的效率和可靠性的基准测试.

主要成果:

基于RNN的NMT模型在预测SARS-CoV-2突变方面的效率和可靠性的演示.
成功应用NMT技术来分析选择的SARS-CoV-2非结构蛋白 (NSP1,NSP3,NSP5,NSP8,NSP9,NSP13,NSP15) 中的突变.

结论:

拟议的NMT方法显示了准确预测病毒突变的前景.
这种预测能力可以显著提高对新兴和潜在更具毒性的SARS-CoV-2变种的准备.
这项研究为应用NMT用于分析长生物序列和理解病毒演变提供了有价值的见解.

关键词:

这就是SARS-CoV-2病毒.有门的经常性单位.长期短期记忆长期短期记忆突变预测突变预测神经机器翻译神经机器翻译经常性的神经网络.

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