通过张量分解预测药物-基因-疾病关联,用于基于网络的计算药物重新定位
Yoonbee Kim1, Young-Rae Cho1,2
1Division of Software, Yonsei University Mirae Campus, Wonju-si 26493, Gangwon-do, Republic of Korea.
Biomedicines
|July 29, 2023
概括
这项研究引入了一种新的基于网络的药物重新定位方法,提高了药物发现效率. 该方法有效地预测了新的药物-基因-疾病关联,改善了治疗识别.
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:
- 药物重新定位通过为现有药物找到新的用途来加速药物发现.
- 计算网络方法对于推断药物与疾病的关联是有价值的.
- 整合药物-基因-疾病关系为重新定位提供了全面的观点.
研究的目的:
- 开发一种基于网络的药物重新定位方法,使用张量分解.
- 预测药物 - 基因 - 疾病三重关联和双重关联.
- 提高药物新疗法指示识别的效率和准确性.
主要方法:
- 构建了一个药物-基因-疾病张量,整合已知的关联.
- 使用集体通用张量分解 (GTD) 和多层感知子 (MLP) 进行预测.
- 用化学结构和ATC代码作为药物特征用于网络建设.
主要成果:
- 整体模型在三重关联预测中获得了0.96的AUC,比现有方法提高了7%.
- 在预测新型药物-基因-疾病关系方面表现出卓越的表现.
- 展示了对对关联预测 (药物-疾病,药物-基因,疾病-基因) 的竞争性准确性.
结论:
- 拟议的基于网络的组合方法显著推进了药物重新定位.
- 结合遗传信息可以提高药物-基因-疾病关联的预测.
- 这种方法为复杂的生物关系提供了更灵活和非线性建模.
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