签署的两个空间近距离模型用于蛋白质-蛋白质相互作用网络中的学习表示
Nikolaos Nakis1, Chrysoula Kosma2, Anastasia Brativnyk3
1École Polytechnique, LIX, Institute Polytechnique de Paris, Palaiseau, 91120, France.
Bioinformatics (Oxford, England)
|April 23, 2025
概括
我们开发了一个新的模型,Signed Two-Space Proximity Model (S2-SPM),以更准确地预测蛋白质与蛋白质相互作用 (PPI). S2-SPM分析了激活和抑制相互作用,改善了我们对生物过程的理解.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 预测蛋白质与蛋白质相互作用 (PPI) 对理解生物过程和疾病至关重要.
- 测定PPI的实验方法是昂贵的;机器学习提供了一个替代方案.
- 签名PPI网络,包括激活和抑制相互作用,需要专门的分析.
研究的目的:
- 为分析已签名的PPI网络引入签名的两个空间近距离模型 (S2-SPM).
- 开发一种明确区分正和负蛋白相互作用的模型.
- 在这些网络中识别代表极端蛋白质配置文件的原型.
主要方法:
- 利用两个独立的潜空间,单独建模正负相互作用.
- 通过在这些潜伏空间中的接近来表示蛋白质相似性.
- 使用链接预测任务和基因本体学 (GO) 丰富分析进行验证.
主要成果:
- 与基线方法相比,S2-SPM在预测相互作用存在和信号方面表现出卓越的性能.
- 丰富分析证实了已识别的原型及其相关的生物任务的生物学相关性.
- 统计学意义,灵敏度分析和BNMI指标证实了模型的稳定性和可靠性.
结论:
- 通过结合签名的PPI,S2-SPM准确地模拟了生物系统中复杂的调节机制.
- 该模型提供了对不同相互作用类型的功能作用的洞察,并确定了关键蛋白质原型.
- 在S2-SPM是免费的,促进进一步的研究在签名网络分析.
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