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

Author:

Li Yinghong1ORCID,Yang Yinqi1,Tong Zhuohao1,Wang Yu1,Mi Qin1,Bai Mingze1,Liang Guizhao23ORCID,Li Bo4ORCID,Shu Kunxian1ORCID

Affiliation:

1. Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications , Chongqing 400065 , P. R. China

2. Key Laboratory of Biorheological Science and Technology , Ministry of Education, Bioengineering College, , Chongqing, 400044 , P. R. China

3. Chongqing University , Ministry of Education, Bioengineering College, , Chongqing, 400044 , P. R. China

4. College of Life Sciences, Chongqing Normal University , Chongqing 401331 , P. R. China

Abstract

Abstract Computational 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 detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Chongqing Postdoctoral Research Project Special Funding

Science Foundation of Chongqing Municipal Commission of Education

Innovation and Entrepreneurship Training Program for College Students in Chongqing

Publisher

Oxford University Press (OUP)

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