可解释性
计算机科学
机器学习
人工智能
推论
药物发现
领域知识
管道(软件)
图形
生物信息学
理论计算机科学
生物
程序设计语言
作者
Zhiqiang Zhong,Davide Mottin
标识
DOI:10.1145/3580305.3599563
摘要
Conventional Artificial Intelligence models are heavily limited in handling complex biomedical structures (such as 2D or 3D protein and molecule structures) and providing interpretations for outputs, which hinders their practical application. Graph Machine Learning (GML) has gained considerable attention for its exceptional ability to model graph-structured biomedical data and investigate their properties and functional relationships. Despite extensive efforts, GML methods still suffer from several deficiencies, such as the limited ability to handle supervision sparsity and provide interpretability in learning and inference processes and their ineffectiveness in utilising relevant domain knowledge. In response, recent studies have proposed integrating external biomedical knowledge into the GML pipeline to realise more precise and interpretable drug discovery with limited training instances.
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