基于残留注意网络和免费的多层次对抗训练的药物重新定位
Guanghui Li1, Shuwen Li2, Cheng Liang3
1School of Information Engineering, East China Jiaotong University, Nanchang, China. ghli16@hnu.edu.cn.
BMC bioinformatics
|August 8, 2024
概括
这项研究介绍了RAFGAE,一种用于预测药物疾病关联的计算方法. 通过分析复杂的网络数据,RAFGAE有效地识别了新的治疗潜力,优于现有的方法.
科学领域:
- 计算机化药物发现.
- 生物信息学是一种生物信息学.
- 网络药理学 网络药理学
背景情况:
- 药物开发的传统实验方法是昂贵的,耗时的和危险的.
- 识别药物与疾病的关联对于药物重新定位和理解疾病的发病过程至关重要.
- 预测药物疾病关系的现有计算方法需要提高准确性和效率.
研究的目的:
- 开发一种新的计算方法来预测药物与疾病的关联.
- 提高药物和疾病之间的潜在治疗关系的确定准确性和效率.
- 克服药物发现中传统实验方法的局限性.
主要方法:
- 将已知的药物疾病关联集成到双边网络中.
- 使用多层图表注意网络 (GAT) 和节点嵌入的残余网络开发Re_GAT框架.
- 使用图形自编码器 (GAE) 进行协作训练和免费的多尺度对抗训练 (FMAT),以提高预测性能.
主要成果:
- 与当前的方法相比,RAFGAE在对基准数据集的十倍交叉验证中表现优越.
- 该方法成功地发现了新的药物疾病关联,由案例研究证实.
- Re_GAT框架通过聚合多节点邻居信息来有效地学习节点嵌入,而FMAT提高了功能质量.
结论:
- 实验结果验证了RAFGAE方法的实用性和准确性.
- RAFGAE显示出作为预测未被观察到的药物疾病关联的强大工具的前景.
- 这种计算方法可以加速药物发现和重新定位的努力.
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