解释蛋白质-蛋白质相互作用与基于知识图的语义相似性
Rita T Sousa1, Sara Silva1, Catia Pesquita1
1LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal.
Computers in biology and medicine
|February 3, 2024
概括
我们介绍KGsim2vec,这是一种用于生物医学研究的新型可解释的人工智能方法. 这种方法通过使用知识图的语义相似性来提高机器学习模型的解释性,改善预测并识别数据偏差.
科学领域:
- 生物医学信息学是生物医学信息学.
- 科学领域的人工智能
- 机器学习用于药物发现.
背景情况:
- 机器学习 (ML) 和人工智能 (AI) 越来越多地用于生物医学应用,如蛋白质-蛋白质相互作用预测.
- 可解释的人工智能 (XAI) 对于科学发现至关重要,能够理解ML机制和数据偏差.
- 知识图 (KGs) 代表域知识,但通常使用无法解释的嵌入式来探索.
研究的目的:
- 开发一种可解释的方法,用于在生物医学应用的知识图中表示实体.
- 提高复杂生物领域的机器学习模型的可解释性和预测性能.
- 为无法解释的知识图嵌入提供替代方案.
主要方法:
- 提出了KGsim2vec,这是一种创新的方法,用于生成可解释的向量表示,使用面向知识图中的面向语义相似性.
- 利用各种机器学习模型 (决策树,遗传编程,随机森林,极端梯度提升) 来预测实体关系.
- 在知识图中跨多个语义方面计算的相似性.
主要成果:
- 在实体相似性表示中考虑多个语义方面,提高了可解释性和预测性能.
- KGsim2vec的性能优于传统的黑子方法,如知识图嵌入和图神经网络.
- 开发的模型能够捕捉生物现象并揭示数据偏差.
结论:
- 与当前基于嵌入的方法相比,KGsim2vec为生物医学应用提供了更容易解释和更有效的方法.
- 该方法通过提供对生物关系和数据特征的可解释的见解来增强科学发现.
- 这项工作推动了可解释AI与知识图的整合,以进行强大的生物医学数据分析.
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