拓驱动的负采样增强了蛋白质-蛋白质相互作用预测的概括性
Ayan Chatterjee1,2,3, Babak Ravandi2,3,4, Parham Haddadi2
1BioClarity AI, Boston, MA 02130, United States.
Bioinformatics (Oxford, England)
|April 7, 2025
概括
我们开发了一种新的机器学习方法,UPNA-PPI,以改善蛋白质-蛋白质相互作用的预测. 这种方法使用新的负样本来提高药物发现的概括性和解释性.
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
- 机器学习 机器学习
背景情况:
- 准确的蛋白质-蛋白质相互作用 (PPI) 预测对于了解疾病和识别药物点至关重要.
- 目前的机器学习 (ML) 模型面临的局限性是由于负样本不足,快捷学习和普遍性差.
研究的目的:
- 引入一种用于蛋白质与蛋白质非相互作用 (PPNI) 战略采样的新方法.
- 开发一个高通量ML管道,UPNA-PPI,以高效地选数十亿次交互.
- 提高PPI预测的概括性和解释性.
主要方法:
- 利用更高层次网络特征进行战略PPNI采样.
- 在UPNA-PPI管道中将无监督的预培训与拓PPNI (TPPNI) 样本集成.
- 在图ML中,利用网络拓洞察力用于负采样方法.
主要成果:
- 使用UPNA-PPI与TPPNI样本对PPI预测的概括性和解释性得到改善.
- 增强了对氨基酸序列上的潜在蛋白质结合部位的识别.
- 促进了ML预测在蛋白质家族和同位体之间可转移性.
结论:
- 在图ML中,UPNA-PPI建立了负采样的基础.
- 该方法加强了对药物发现的查试验的优先考虑.
- 这种方法推进了蛋白质相互作用预测和分析领域.
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