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%.

翻译：流感病毒的快速突变威胁着公共卫生安全。不同宿主间的病毒重配可能导致致命的大流行。然而，由于流感病毒可在不同物种间传播，在疫情暴发期间或之后检测病毒的原始宿主十分困难。因此，早期快速检测病毒宿主有助于减少病毒的进一步传播。我们采用多种机器学习模型，使用基于位置特异性评分矩阵（PSSM）的特征以及从词嵌入和词编码中学习到的特征，来推断病毒的起源宿主。结果表明，基于PSSM的模型性能达到约95%的MCC和约96%的F1值。使用词嵌入的模型获得的MCC约为96%，F1值约为97%。