通过多源疾病相似性网络改进计算药物重新定位
1School of Information and Communications Technology, Hanoi University of Science and Technology, No. 1 Dai Co Viet, Hai Ba Trung, Hanoi, Vietnam. hauld@soict.hust.edu.vn.
Scientific reports
|August 21, 2025
概括
这项研究引入了一种使用综合疾病网络的新计算药物重新定位方法,大大提高了新药物与疾病的相关性. 这种方法优于现有的方法,并确定了临床上相关的药物疾病联系.
科学领域:
- 计算生物学
- 药理学
- 生物信息学
背景情况:
- 计算药物重新定位可以确定现有药物的新用途.
- 基于网络的方法整合了药物,疾病和目标信息.
- 单一疾病的相似性网络限制了信息多样性.
研究的目的:
- 开发一种先进的计算药物重新定位方法.
- 整合多种疾病相似性网络以改善预测.
- 确定具有临床意义的新药与疾病的关联.
主要方法:
- 构建了表型,本体和分子疾病相似性网络.
- 整合到多重疾病和多重异质网络.
- 应用了定制的随机步行与重启 (RWR) 算法进行预测.
主要成果:
- 多层网络和多层异质网络的性能优于单层网络.
- 在十倍交叉验证方面, MHDR 方法超过了最先进的方法.
- 确定了许多新的药物疾病关联,包括由共享蛋白质,途径和蛋白质复合体支持的.
结论:
- 整合多种疾病相似性网络可以提高药物重新定位的准确性.
- 这种方法为发现新疗法提供了强有力的工具.
- 预测的关联显示出实际影响,许多通过临床试验得到验证.
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