论文标题
使用PSSM和单词嵌入来预测流感的病毒宿主
Predicting Influenza A Viral Host Using PSSM and Word Embeddings
论文作者
论文摘要
流感病毒的快速突变威胁着公共卫生。在患有不同宿主的病毒之间进行重新分类会导致致命的大流行。但是，由于流感病毒可以在不同物种之间循环，因此很难在爆发期间或爆发后检测到该病毒的原始宿主。因此，病毒宿主的早期和快速检测将有助于减少病毒的进一步扩散。我们使用各种机器学习模型，这些模型具有从特定位置的评分矩阵（PSSM）中得出的功能，以及从单词嵌入和单词编码中学到的特征来推断病毒的原始宿主。结果表明，基于PSSM的模型的性能达到了95％左右的MCC，而F1的性能约为96％。使用单词嵌入模型获得的MCC约为96％，F1约为97％。
The rapid mutation of the 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%.
