在生物研究中用于可解释AI的路径引导架构
Qi Zhou1, Naga Sekhar Madala2, Chen Huang1,3
1Department of Genetics, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama.
Computational and structural biotechnology journal
|November 24, 2025
概括
路径引导的可解释深度学习架构 (PGI-DLA) 集成路径知识,以改进多omics数据的深度学习模型. 本综述指导选择途径数据库,以获得更好的生物和临床见解.
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
- 系统生物学 系统生物学
背景情况:
- 深度学习 (DL) 模型擅长分析复杂的生物多omics数据.
- DL的"黑盒子"性质阻碍了生物和临床翻译.
- 知识导向DL,特别是路径导向可解释深度学习架构 (PGI-DLA),通过整合先前的路径知识来解决这一问题.
研究的目的:
- 审查PGI-DLA在多omics数据分析方面的进展.
- 为了比较主要的途径数据库 (KEGG,GO,Reactome,MSigDB) 它们在PGI-DLA中的适用性.
- 为选择和优化用于PGI-DLA实施的路径数据库提供指导.
主要方法:
- 审查关于PGI-DLA方法的当前文献.
- 基于知识范围,结构,细节和策划的途径数据库的比较分析.
- 讨论数据库选择对模型设计,性能和可解释性的影响.
主要成果:
- 通过结合生物路径信息,PGI-DLA提高了DL模型的性能和可解释性.
- 途径数据库之间存在显著差异,影响了它们在PGI-DLA中的应用.
- 选择合适的路径数据库对于成功实施PGI-DLA至关重要.
结论:
- PGI-DLA提供了一种有前途的方法,用于将多学科数据转化为生物和临床见解.
- 仔细考虑和优化路径数据库对于最大限度地发挥PGI-DLA的好处至关重要.
- 本综述是研究人员将PGI-DLA应用于复杂疾病的宝贵资源.
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