HTINet2:通过知识图嵌入和残余类图的神经网络进行草药目标预测
Pengbo Duan1, Kuo Yang1, Xin Su1
1Institute of Medical Intelligence, Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China.
这项研究介绍了HTINet2,这是一种用于预测草药目标的深度学习框架. HTINet2显著提高了确定药物发现和理解草药机制的治疗点的准确性.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 准确的目标识别在药物开发中至关重要,以了解药物机制并发现新的治疗点.
- 现有的草药标预测方法面临的挑战是由于不完整的临床知识和无监督模型的局限性.
研究的目的:
- 开发一个先进的深度学习框架,HTINet2,用于准确的草药目标预测.
- 解决当前草药标识方法中的数据和模型限制.
主要方法:
- 构建了一个大规模的知识图,整合了传统中医药 (TCM) 的特性和临床治疗数据.
- 使用深度知识嵌入来学习草药和目标的表示.
- 利用剩余图形卷积网络进行交互学习和贝叶斯个性化排名损失进行监督预测.
主要成果:
- 与基线方法相比,HTINet2表现优越,HR@10增加了122.7%,NDCG@10增加了35.7%.
- 废弃性研究证实了HTINet2.2中的单个模块的积极贡献.
- 对Artemisia annua和Coptis chinensis的案例研究使用文献和分子对接验证了预测目标的可靠性.
结论:
- HTINet2提供了一个强大的深度学习框架,以提高草药目标预测的准确性.
- 该框架有效地整合了各种知识来源和先进的网络架构.
- 对于加速药物发现和阐明草药作用机制,HTINet2显示出前景.
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