Drug repositioning based on individual bi-random walks on a heterogeneous network
Abstract Background Traditional drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing drugs, referred as drug repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods h...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-019-3117-6 |
| _version_ | 1818565119036096512 |
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| author | Yuehui Wang Maozu Guo Yazhou Ren Lianyin Jia Guoxian Yu |
| author_facet | Yuehui Wang Maozu Guo Yazhou Ren Lianyin Jia Guoxian Yu |
| author_sort | Yuehui Wang |
| collection | DOAJ |
| description | Abstract Background Traditional drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing drugs, referred as drug repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods have been proposed for drug repositioning and most of them apply random walk on a heterogeneous network consisted with disease and drug nodes. However, these methods generally adopt the same walk-length for all nodes, and ignore the different contributions of different nodes. Results In this study, we propose a drug repositioning approach based on individual bi-random walks (DR-IBRW) on the heterogeneous network. DR-IBRW firstly quantifies the individual work-length of random walks for each node based on the network topology and knowledge that similar drugs tend to be associated with similar diseases. To account for the inner structural difference of the heterogeneous network, it performs bi-random walks with the quantified walk-lengths, and thus to identify new indications for approved drugs. Empirical study on public datasets shows that DR-IBRW achieves a much better drug repositioning performance than other related competitive methods. Conclusions Using individual random walk-lengths for different nodes of heterogeneous network indeed boosts the repositioning performance. DR-IBRW can be easily generalized to prioritize links between nodes of a network. |
| first_indexed | 2024-12-14T01:37:08Z |
| format | Article |
| id | doaj.art-1c4b4465fadc4595b372152bffd95b18 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T01:37:08Z |
| publishDate | 2019-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-1c4b4465fadc4595b372152bffd95b182022-12-21T23:21:51ZengBMCBMC Bioinformatics1471-21052019-12-0120S1511310.1186/s12859-019-3117-6Drug repositioning based on individual bi-random walks on a heterogeneous networkYuehui Wang0Maozu Guo1Yazhou Ren2Lianyin Jia3Guoxian Yu4College of Computer and Information Sciences, Southwest UniversitySchool of Electrical and Information Engineering, Beijing University of Civil Engineering and ArchitectureSchool of Computer Science and Engineering, University of Electronic Science and Technology of ChinaCollege of Information Engineering and Automation, Kunming University of Science and TechnologyCollege of Computer and Information Sciences, Southwest UniversityAbstract Background Traditional drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing drugs, referred as drug repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods have been proposed for drug repositioning and most of them apply random walk on a heterogeneous network consisted with disease and drug nodes. However, these methods generally adopt the same walk-length for all nodes, and ignore the different contributions of different nodes. Results In this study, we propose a drug repositioning approach based on individual bi-random walks (DR-IBRW) on the heterogeneous network. DR-IBRW firstly quantifies the individual work-length of random walks for each node based on the network topology and knowledge that similar drugs tend to be associated with similar diseases. To account for the inner structural difference of the heterogeneous network, it performs bi-random walks with the quantified walk-lengths, and thus to identify new indications for approved drugs. Empirical study on public datasets shows that DR-IBRW achieves a much better drug repositioning performance than other related competitive methods. Conclusions Using individual random walk-lengths for different nodes of heterogeneous network indeed boosts the repositioning performance. DR-IBRW can be easily generalized to prioritize links between nodes of a network.https://doi.org/10.1186/s12859-019-3117-6Drug repositioningDrug-disease heterogeneous networkIndividual walk-lengthBi-random walks |
| spellingShingle | Yuehui Wang Maozu Guo Yazhou Ren Lianyin Jia Guoxian Yu Drug repositioning based on individual bi-random walks on a heterogeneous network BMC Bioinformatics Drug repositioning Drug-disease heterogeneous network Individual walk-length Bi-random walks |
| title | Drug repositioning based on individual bi-random walks on a heterogeneous network |
| title_full | Drug repositioning based on individual bi-random walks on a heterogeneous network |
| title_fullStr | Drug repositioning based on individual bi-random walks on a heterogeneous network |
| title_full_unstemmed | Drug repositioning based on individual bi-random walks on a heterogeneous network |
| title_short | Drug repositioning based on individual bi-random walks on a heterogeneous network |
| title_sort | drug repositioning based on individual bi random walks on a heterogeneous network |
| topic | Drug repositioning Drug-disease heterogeneous network Individual walk-length Bi-random walks |
| url | https://doi.org/10.1186/s12859-019-3117-6 |
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