用遗传算法优化全球网络对齐:利用蛋白质序列和基因本体学术语的预训练嵌入
IEEE transactions on computational biology and bioinformatics
|August 14, 2025
概括
通过使用遗传算法和蛋白质序列嵌入,GA2Vec在全球范围内对齐多个蛋白质-蛋白质相互作用 (PPI) 网络. 这种新的方法提高了理解复杂生物网络的准确性和效率.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 网络科学 网络科学
背景情况:
- 蛋白与蛋白相互作用 (PPI) 网络对于理解细胞过程至关重要.
- 现有的调整PPI网络的方法在平衡精度和计算效率方面面临挑战.
- 确定跨物种网络相似性和预测蛋白质复合体是关键目标.
研究的目的:
- 引入GA2Vec,这是一个新的方法,可以以许多对许多的方式在全球范围内对齐多个PPI网络.
- 利用先进的嵌入技术重建加权的PPI网络并结合功能相似性.
- 使用遗传算法优化网络对齐,以提高准确性和效率.
主要方法:
- 对于蛋白质序列,GA2Vec使用来自ProtBERT,ESM-2和ProtT5-XL-UniRef50的矢量嵌入.
- 来自Anc2vec的基因本体学 (GO) 术语嵌入被纳入以捕捉功能相似性.
- 一个遗传算法通过基于嵌入相似性的健身函数来改进由社区检测算法生成的候选集群.
主要成果:
- GA2Vec实现了跨多种生物网络 (真核生物,原核生物,SARS-CoV,病毒宿主) 的强大的全球网络对齐.
- 证明了SARS-CoV-2和SARS-CoV-1 PPI网络之间的有效对齐.
- 平衡关键指标,包括F1分数,集群交互质量 (CIQ),内部集群质量 (ICQ),一致的集群和灵敏度.
结论:
- GA2Vec为全球PPI网络对齐提供了有效和可适应的解决方案.
- 该方法成功地整合了序列和功能信息,以进行增强的网络分析.
- GA2Vec为比较网络生物学和理解病毒与主机相互作用提供了一个强大的框架.
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