计算机科学
特征学习
图形
人工智能
编码器
机器学习
特征(语言学)
对偶(语法数字)
理论计算机科学
语言学
操作系统
文学类
哲学
艺术
作者
Luhe Zhuang,Hong Wang,Jun Zhao,Yanshen Sun
标识
DOI:10.1016/j.ins.2023.119139
摘要
Adverse Drug Reactions (ADRs) resulting from drug combinations can endanger patients' health and life. In this paper, we propose ADGCL, a novel Adaptive Dual Graph Contrastive Learning based on heterogeneous signed networks for predicting ADRs. Specifically, ADGCL first explicitly models positive and negative drug-drug interactions (DDIs) using a heterogeneous signed network to learn semantic-rich drug feature representations. Additionally, ADGCL employs a dual graph contrastive learning strategy including self-supervised contrastive learning and micro-supervised learning to learn high-level features, which significantly improves prediction performance. Furthermore, ADGCL incorporates an adaptive negative sampling method to generate negative samples. Lastly, an encoder based on the implicit graph neural network is designed to capture long-range dependencies in underlying networks, thereby improving the quality of drug feature representation. Comprehensive experimental results on real-world datasets demonstrate that ADGCL is a promising ADR prediction model.
科研通智能强力驱动
Strongly Powered by AbleSci AI