用生物序列预测病毒宿主，准确率超89%。
BiLSTM-VHP: BiLSTM-Powered Network for Viral Host Prediction
- 用双向LSTM分析400碱基病毒序列，自动识别宿主
- 对汉坦病毒准确率达89.62%，轮状病毒达96.58%
- 提供三个病毒数据集，适合流行病预警研究
人类与动物长期共存，但许多动物携带可传染人类的病毒，引发人畜共患病。近年爆发的新冠病毒、猴痘和猪流感凸显了快速准确预测病毒宿主的重要性。本文提出BiLSTM-VHP，一种轻量级双向长短期记忆网络，基于400碱基长度的核苷酸序列，实现对正汉坦病毒、狂犬病病毒和轮状病毒A的宿主预测。模型在正汉坦病毒上达到89.62%准确率，轮状病毒A达96.58%，狂犬病病毒77.22%，优于以往研究。通过混淆矩阵、F1分数、精确率、召回率及微平均AUC评估性能。同时构建了三个高质量数据集：正汉坦病毒（8,575条）、轮状病毒A（95,197条）、狂犬病病毒（22,052条），分别对应9、12、29个宿主类别。代码与数据已公开。
原文摘要 · Abstract (English)
Recorded history shows the long coexistence of humans and animals, suggesting it began much earlier. Despite some beneficial interdependence, many animals carry viral diseases that can spread to humans. These diseases are known as zoonotic diseases. Recent outbreaks of SARS-CoV-2, Monkeypox and swine flu viruses have shown how these viruses can disrupt human life and cause death. Fast and accurate predictions of the host from which the virus spreads can help prevent these diseases from spreading. This work presents BiLSTM-VHP, a lightweight bidirectional long short-term memory (LSTM)-based architecture that can predict the host from the nucleotide sequence of orthohantavirus, rabies lyssavirus, and rotavirus A with high accuracy. The proposed model works with nucleotide sequences of 400 bases in length and achieved a prediction accuracy of 89.62% for orthohantavirus, 96.58% for rotavirus A, and 77.22% for rabies lyssavirus outperforming previous studies. Moreover, performance of the model is assessed using the confusion matrix, F-1 score, precision, recall, microaverage AUC. In addition, we introduce three curated datasets of orthohantavirus, rotavirus A, and rabies lyssavirus containing 8,575, 95,197, and 22,052 nucleotide sequences divided into 9, 12, and 29 host classes, respectively. The codes and dataset are available at https://doi.org/10.17605/OSF.IO/ANFKR
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