A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

Brief Bioinform. 2024 Mar 27;25(3):bbae172. doi: 10.1093/bib/bbae172.ABSTRACTComputational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the d...

Source: Briefings in Bioinformatics - April 22, 2024 Category: Bioinformatics Authors: Yinghong Li Yinqi Yang Zhuohao Tong Yu Wang Qin Mi Mingze Bai Guizhao Liang Bo Li Kunxian Shu Source Type: research