通过对药物和疾病网络的深入分析,推断药物和疾病的关联
Lei Chen1, Kaiyu Chen1, Bo Zhou2
1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
这项研究引入了一种可靠的负样本选择方案 (RNSS) 用于药物重新定位. RNSS提高了药物疾病关联预测的准确性,明显优于现有方法.
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:
- 药物开发是昂贵和低效的,年审批率很低.
- 药物重新定位通过重新定位现有药物提供了一种加速的方法.
- 计算预测药物疾病关联 (DDAs) 是关键的,但负样本选择是一个挑战.
研究的目的:
- 为药物重新定位提出一种新的可靠的负样本选择方案 (RNSS).
- 为了提高计算药物疾病关联预测的准确性.
- 解决现有的DDA预测方法中不可靠的负样本的常见缺陷.
主要方法:
- 开发了RNSS,考虑药物网络中的k-邻居,他们的疾病关联以及药物信息.
- 实施了一个评分系统来评估药物-疾病对,用于DDA预测.
- 使用了三个分类算法 (随机森林,贝叶斯网络,最近邻居) 与RNSS选择的负样本.
主要成果:
- 使用RNSS构建的分类器在交叉验证中表现出几乎完美的性能.
- RNSS显著超过了传统和以前的负面抽样选择方案.
- 拟议的方案有效地确定可靠的负面药物疾病对.
结论:
- 在药物重新定位过程中,RNSS是一种高效的方法来选择可靠的负样本.
- 这种方法大大提高了DDA预测模型的性能.
- RNSS为加速药物发现和开发提供了一种有价值的工具.
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