GCNGAT:基于图形卷积神经网络和图形注意力网络的药物疾病关联预测
Runtao Yang1, Yao Fu1, Qian Zhang2
1School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
Artificial intelligence in medicine
|March 29, 2024
概括
这项研究引入了一种新的图形卷积和注意网络 (GCNGAT) 模型,通过区分背景信息来预测药物疾病关联,改善药物重新定位和发展.
科学领域:
- 计算机化药物发现和开发.
- 网络药理学和系统生物学.
背景情况:
- 准确预测药物与疾病的关联对于确定现有药物的新治疗用途至关重要.
- 现有的方法往往无法区分特定于药物与不同疾病相关的背景信息.
- 这种限制阻碍了精确的药物重新定位和有针对性的药物开发.
研究的目的:
- 开发一种先进的药物-疾病关联预测模型,以考虑疾病特定的背景信息.
- 通过利用图形卷积和注意力网络来提高药物重新定位的准确性.
- 通过提供可靠的关联数据来改善药物研究和开发.
主要方法:
- 构建一个包含已知关联的异质药物疾病图.
- 提取疾病特定的子图,以捕获每个药物-疾病对的独特背景信息.
- 开发一个图形神经网络与全球平均汇集 (GnnAp) 模型的特征表示学习和预测.
主要成果:
- 拟议的GCNGAT模型,包含子图提取,显著提高了预测性能.
- 图形表示学习模块有效提取深层药物-疾病相互作用特征.
- 在接收器运行特征曲线 (AUC) 下实现高面积值为0.9182 (PREDICT) 和0.9417 (CDataset).
- 在PREDICT数据集上表现比最先进的PSGCN模型高1.58%AUC.
结论:
- 亚图提取是提高药物疾病关联预测准确性的关键因素.
- GCNGAT模型为药物重新定位提供了强大的框架,并加速了药物发现.
- 这种方法为制药研发提供了有价值的实验参考.
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