结合:通过知识图驱动的机器学习发现大规模的生物相互作用网络
Naafey Aamer1, Muhammad Nabeel Asim2,3, Aamer Iqbal Bhatti4
1Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany. naafey.aamer@dfki.de.
Journal of translational medicine
|July 31, 2025
概括
BIND集成了多样化的生物相互作用,用于全面的网络分析,加速药物发现. 这种人工智能框架预测和分析多种关系类型,优于生物洞察的孤立方法.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 网络科学 网络科学
背景情况:
- 生物系统包括复杂的,相互连接的网络,对于疾病理解和治疗至关重要.
- 目前的人工智能交互预测器是孤立的,缺少整体网络效应.
- 湿实验室验证是昂贵和耗时的,需要先进的计算工具.
研究的目的:
- 开发一个统一的平台来预测和分析各种生物相互作用.
- 克服孤立预测方法和湿实验室方法的局限性.
- 为了促进治疗开发的综合生物网络分析.
主要方法:
- 开发了BIND (生物交互网络发现),一个使用11种知识图嵌入方法的框架.
- 采用了两阶段的培训策略来解决阶级不平衡和异质性的问题.
- 集成实体嵌入到7个机器学习分类器中,创建了1,050个预测管道.
主要成果:
- 简单的嵌入模型有效地捕获生物模式,往往超过复杂的方法.
- 两阶段训练提高了蛋白质-蛋白质相互作用预测高达26.9%.
- 最佳BIND管道实现了高F1得分 (0.85-0.99);在药物表型案例研究中产生了1355个高可信度预测.
结论:
- BIND提供了一个统一的网络应用程序,用于同时预测和分析多种生物相互作用类型.
- 该平台的表现优于生物网络分析的孤立方法.
- 通过使新型相互作用的实验验证,BIND加速生物标志物发现和治疗开发.
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