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。来自不同宿主的病毒的重新排序可能导致致命的大流行病。然而，在爆发期间或之后检测病毒的原始宿主是困难的，因为流感病毒可以在不同的物种之间循环。因此，早期和快速检测病毒宿主将有助于减少病毒的进一步传播。我们使用来自位置特异性打分矩阵（PSSM）的特征和从词嵌入和词编码中学习到的特征，结合各种机器学习模型来推断病毒的起源宿主。结果显示，基于PSSM模型的性能达到了约95%的MCC，约96%的F1；使用词向量模型的MCC约为96%，F1约为97%。