基于双通道神经网络的毒性因子的分类,使用预先训练的语言模型
Guanghui Li1, Peiyang Song1, Jiawei Luo2
1School of Information and Software Engineering, East China Jiaotong University, Nanchang, China.
PloS one
|January 6, 2026
概括
这项研究引入了PLM-GNN,一种用于分类毒性因子 (VF) 的新模型. 它准确地确定了七个主要的VF,有助于打击传染病.
科学领域:
- 微生物学和生物信息学
- 计算生物学 计算生物学
- 传染病研究 传染病研究
背景情况:
- 毒性因子 (VF) 是关键的分子,使病原体能够通过逃避宿主免疫来引起疾病.
- 由于抗生素耐药性增加和新出现的传染病原体,准确地对VF进行分类变得越来越重要.
- 了解VF功能对于开发新型治疗策略至关重要.
研究的目的:
- 开发和验证一种创新的双通道模型,PLM-GNN,用于精确分类七种最多的毒性因子类型.
- 为了提高分类准确性,利用VF的结构和顺序信息.
- 为推进病毒性因子函数研究提供一个计算工具.
主要方法:
- PLM-GNN集成了一个结构通道,使用几何图形神经网络来分析3D结构特征.
- 序列通道使用预训练的语言模型与CNN和变压器架构来提取本地和全球序列特征.
- 该模型在一个独立的测试集上对分类性能进行了评估.
主要成果:
- 在独立测试组中,PLM-GNN在独立测试组中实现了高精度的86.47%.
- 该模型的F1得分高达86.20%,AUC为97.20%.
- 这些结果验证了VF分类的双通道方法的有效性.
结论:
- PLM-GNN准确地分类了七个主要的毒性因素.
- 该模型为研究VF函数提供了一种新且有效的计算方法.
- 这些发现有助于更好地了解病原体的毒性和疾病机制.
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