Predicting Influenza A Viral Host Using PSSM and Word Embeddings

2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) Pub Date : 2021-10-13 DOI:10.1109/CIBCB49929.2021.9562959

Yanhua Xu, D. Wojtczak

引用次数: 1

Abstract

The rapid mutation of influenza virus threatens public health. Reassortment among viruses with different hosts can lead to a fatal pandemic. However, it is difficult to detect the original host of the virus during or after an outbreak as influenza viruses can circulate between different species. Therefore, early and rapid detection of the viral host would help reduce the further spread of the virus. We use various machine learning models with features derived from the position-specific scoring matrix (PSSM) and features learned from word embedding and word encoding to infer the origin host of viruses. The results show that the performance of the PSSM-based model reaches the MCC around 95%, and the F1, around 96%. The MCC obtained using the model with word embedding is around 96%, and the F1 is around 97%.

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利用PSSM和词嵌入预测甲型流感病毒宿主

流感病毒的快速变异威胁着公众健康。不同宿主的病毒重新组合可能导致致命的大流行。然而，由于流感病毒可以在不同物种之间传播，因此很难在疫情期间或之后发现病毒的原始宿主。因此，早期和快速发现病毒宿主将有助于减少病毒的进一步传播。我们使用各种机器学习模型，这些模型具有来自位置特定评分矩阵(PSSM)的特征，以及从单词嵌入和单词编码中学习的特征来推断病毒的起源宿主。结果表明，基于pssm的模型的MCC性能达到95%左右，F1性能达到96%左右。采用词嵌入模型得到的MCC约为96%，F1约为97%。

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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2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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