Abstract
Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.
Highlights
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Abstract: Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development
Based on the fact that cross-talks among functional modules could play important roles in drug response and disease progression, we proposed a computational method, which used module network to identify essential modules in disease progression and improve connectivity map (CMap) drug screening strategy, to do drug repositioning
We obtained 486 significant pairs among 116 modules, which were shown in Figure S3 and Supplementary Table S2. (ii) For each disease, the perturbation of the genes was calculated based on the gene expression data of disease samples and control samples, and mapped the perturbation of the genes to the module’s space through an index Imp (Method) to obtain the initial score of the modules
Summary
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Abstract: Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. Most of the existing methods neglect the gene modules and the interactions among the modules, the cross-talks among pathways are common in drug response. We developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response
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