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arXiv 2108.04240q-bio.QMcs.LG
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基于卷积神经网络的流感病毒血凝素蛋白质序列分类

Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks

Computation-based Science and Technology Research Center, The Cyprus Institute(塞浦路斯研究所计算科学与技术研究中心);Faculty of Computing, Engineering and the Built Environment, Birmingham City University(伯明翰城市大学计算、工程与建筑环境学院)

Charalambos Chrysostomou, Floris Alexandrou, Mihalis A. Nicolaou, Huseyin Seker

摘要:

流感病毒可以被认为是最严重的病毒之一,可以感染多个物种,对宿主造成致命的后果。该病毒的血凝素(HA)基因可以作为抗病毒药物开发的靶点,通过准确鉴定其亚型和可能的靶向宿主来实现。本文主要研究仅利用HA基因的蛋白质序列,准确预测A型流感病毒是否能感染特定的宿主,更具体地说,人、禽和猪宿主。更详细地说,我们建议使用疏水指数将蛋白质序列编码成数字信号,随后利用基于卷积神经网络的预测模型。用于拟定工作的流感HA蛋白质序列获自流感研究数据库(IRD)。具体而言,完整和独特的HA蛋白质序列用于禽类、人类和猪宿主。本工作获得的数据是17999个人宿主蛋白,17667个禽宿主蛋白和9278个猪宿主蛋白。鉴于这组收集的蛋白质,所提出的方法产生高达10%的准确性高出一个单独的类(即,禽)和5%的整体准确性比在早期的研究。还观察到,在这项工作中,每个类的准确性比以前的研究中提出的更平衡。实验结果表明,该模型能够很好地区分人、禽、猪三种宿主的HA蛋白质序列。

英文摘要:

The Influenza virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Hemagglutinin (HA) gene of the virus can be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. This paper focuses on accurately predicting if an Influenza type A virus can infect specific hosts, and more specifically, Human, Avian and Swine hosts, using only the protein sequence of the HA gene. In more detail, we propose encoding the protein sequences into numerical signals using the Hydrophobicity Index and subsequently utilising a Convolutional Neural Network-based predictive model. The Influenza HA protein sequences used in the proposed work are obtained from the Influenza Research Database (IRD). Specifically, complete and unique HA protein sequences were used for avian, human and swine hosts. The data obtained for this work was 17999 human-host proteins, 17667 avian-host proteins and 9278 swine-host proteins. Given this set of collected proteins, the proposed method yields as much as 10% higher accuracy for an individual class (namely, Avian) and 5% higher overall accuracy than in an earlier study. It is also observed that the accuracy for each class in this work is more balanced than what was presented in this earlier study. As the results show, the proposed model can distinguish HA protein sequences with high accuracy whenever the virus under investigation can infect Human, Avian or Swine hosts.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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