Li Xianbin, Zan Xiangzhen, Liu Tao, Dong Xiwei, Zhang Haqi, Li Qizhang, Bao Zhenshen, Lin Jie
School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China.
Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
iScience. 2024 May 18;27(7):110025. doi: 10.1016/j.isci.2024.110025. eCollection 2024 Jul 19.
Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.
药物重新利用是为已获批药物寻找新治疗适应症的一种有前景的方法。已经提出了许多计算方法，通过基因或通路水平对候选抗癌药物进行优先级排序。然而，这些方法忽略了边缘水平上基因相互作用的变化。为了解决这一局限性，我们基于边缘信息和通路拓扑结构开发了一种计算药物重新利用方法（iEdgePathDDA）。首先，我们使用皮尔逊相关系数识别通路内药物诱导的和疾病相关的边缘（基因相互作用的变化）。接下来，我们计算药物诱导边缘与疾病相关边缘之间的抑制分数。最后，我们根据所有疾病相关边缘上的抑制分数对候选药物进行优先级排序。案例研究表明，我们的方法基于CTD数据库成功识别了新的药物-疾病对。与现有方法相比，结果表明我们的方法在结直肠癌、乳腺癌和肺癌数据集的五个指标方面具有卓越的性能。
