College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.
Math Biosci Eng. 2023 Jun 26;20(8):14136-14157. doi: 10.3934/mbe.2023632.
Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes.
药物对于人类健康至关重要，可治疗各种疾病。然而，开发新药非常费力、耗时且昂贵。尽管近年来对药物开发的投资大大增加，但每年获得批准的药物数量仍然相当低。药物重定位被认为是加速药物开发程序的有效手段，因为它可以发现现有药物的新作用。在药物重定位中已经提出了许多计算方法，其中一些被设计为可以预测药物-疾病关联（DDA）的二进制分类器。该方法的一个常见缺陷是负样本选择。在这项研究中，提出了一种新颖的可靠负样本选择方案，称为 RNSS，它可以筛选出与实际 DDA 概率低的可靠药物-疾病对。该方案考虑了药物网络中一种药物的 k-近邻的信息，包括它们与疾病和药物的关联。然后，建立了一个评分系统来评估药物-疾病对。为了测试 RNSS 的效用，使用三种经典分类算法（随机森林、贝叶斯网络和最近邻算法），使用 RNSS 选择的负样本构建分类器。交叉验证结果表明，这些分类器提供了近乎完美的性能，明显优于使用一些传统和先前的负样本选择方案的分类器。
