不同质的图形对比学习与渐变平衡用于药物重新定位
Hai Cui1, Meiyu Duan1, Haijia Bi2
1Information Science and Technology College, Dalian Maritime University, No.1 Linghai Road, Dalian 116026, Liaoning, China.
这项研究引入了GCGB,一种新的图形对比学习方法,通过预测药物和疾病的关联来改善药物重新定位. 它通过平衡任务和整合多样化的数据视图来增强学习,以便更准确地发现药物.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 药物重新定位通过为现有药物找到新的用途来加速药物发现.
- 药物与疾病相关性 (DDA) 预测中的标签稀缺性是一个关键挑战.
- 图形对比学习 (GCL) 提供了一种有希望的方法,通过生成自我监督的信号来增强DDA预测.
研究的目的:
- 为改进DDA预测提出一种具有梯度平衡 (GCGB) 的新型异质图对比学习方法.
- 解决现有的GCL方法在增强视图生成和任务优化不平衡方面的局限性.
主要方法:
- 引入了融合观点,整合了药物/疾病相似性网络和异质生物医学网络.
- 设计了面试视图对比的学习任务,以对比融合,语义和交互视图.
- 实现了自适应式梯度平衡,以优化辅助和主要任务.
主要成果:
- 拟议的GCGB方法有效地捕捉了更高阶交互语义.
- 梯度平衡改善了优化,并将参数更新引导到主要的DDA预测任务.
- 三个基准的实验证明了GCGB的有效性.
结论:
- GCGB为药物重新定位和DDA预测提供了一个强大的框架.
- 该方法成功地解决了视图生成和任务优化方面的挑战.
- GCGB显示出加速识别新疗法指示的巨大潜力.
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