基于深度稀疏自编码器和药物疾病相似性的药物重新定位
Song Lei1, Xiujuan Lei2, Ming Chen3
1School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
这项研究介绍了DRDSA,这是一种使用深度稀疏自编码器分析复杂药物疾病网络的新型药物重新定位方法. DRDSA实现了最佳的预测结果,并确定了潜在的COVID-19抗病毒药物.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 药物重新定位通过确定现有药物的新用途来加速药物开发.
- 以前的方法在药物疾病分析中与复杂的异质网络作斗争.
- 开发先进的计算模型对于有效的药物重新定位至关重要.
研究的目的:
- 引入DRDSA,一种新的药物重新定位方法.
- 克服现有模型在处理复杂的药物疾病网络方面的局限性.
- 提高预测新的药物疾病关联的准确性和效率.
主要方法:
- 构建了一个药物-疾病特征网络,集成化学结构,疾病语义和已知的关联.
- 采用深度稀疏的自动编码器来进行低维表示学习.
- 利用深度神经网络来预测新的药物疾病关联.
主要成果:
- 在四个基准数据集中,DRDSA取得了最佳的结果,超过了基准方法.
- 在CTD数据集上表现出高性能,AUC为0.9619和AUPR为0.9676.
- 成功预测了COVID-19的顶级抗病毒药物,其中六种被文献验证.
结论:
- 对于药物重新定位,DRDSA提供了一种强大而准确的方法.
- 该方法在药物疾病分析中有效处理复杂的异质网络.
- 对于加速药物发现,DRDSA具有显著的潜力,包括为COVID-19等新出现的疾病确定治疗方法.
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