Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction
Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the medical field. Here, based on heterogeneous netw...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-08-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.720327/full |
| _version_ | 1818358591777669120 |
|---|---|
| author | Jia Qu Chun-Chun Wang Shu-Bin Cai Wen-Di Zhao Xiao-Long Cheng Zhong Ming |
| author_facet | Jia Qu Chun-Chun Wang Shu-Bin Cai Wen-Di Zhao Xiao-Long Cheng Zhong Ming |
| author_sort | Jia Qu |
| collection | DOAJ |
| description | Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the medical field. Here, based on heterogeneous networks built on the information of known miRNA–disease associations, miRNA function similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, we developed a computing model of biased random walk with restart on multilayer heterogeneous networks for miRNA–disease association prediction (BRWRMHMDA) through enforcing degree-based biased random walk with restart (BRWR). Assessment results reflected that an AUC of 0.8310 was gained in local leave-one-out cross-validation (LOOCV), which proved the calculation algorithm’s good performance. Besides, we carried out BRWRMHMDA to prioritize candidate miRNAs for esophageal neoplasms based on HMDD v2.0. We further prioritize candidate miRNAs for breast neoplasms based on HMDD v1.0. The local LOOCV results and performance analysis of the case study all showed that the proposed model has good and stable performance. |
| first_indexed | 2024-12-13T20:31:26Z |
| format | Article |
| id | doaj.art-e06f46ecd40a4e73a538d0a92c94d3d1 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-13T20:31:26Z |
| publishDate | 2021-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-e06f46ecd40a4e73a538d0a92c94d3d12022-12-21T23:32:25ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-08-011210.3389/fgene.2021.720327720327Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association PredictionJia Qu0Chun-Chun Wang1Shu-Bin Cai2Wen-Di Zhao3Xiao-Long Cheng4Zhong Ming5School of Computer Science and Artificial Intelligence & Aliyun School of Big Data, Changzhou University, Changzhou, ChinaInformation and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaSchool of Computer Science and Artificial Intelligence & Aliyun School of Big Data, Changzhou University, Changzhou, ChinaSchool of Computer Science and Artificial Intelligence & Aliyun School of Big Data, Changzhou University, Changzhou, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaNumerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the medical field. Here, based on heterogeneous networks built on the information of known miRNA–disease associations, miRNA function similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, we developed a computing model of biased random walk with restart on multilayer heterogeneous networks for miRNA–disease association prediction (BRWRMHMDA) through enforcing degree-based biased random walk with restart (BRWR). Assessment results reflected that an AUC of 0.8310 was gained in local leave-one-out cross-validation (LOOCV), which proved the calculation algorithm’s good performance. Besides, we carried out BRWRMHMDA to prioritize candidate miRNAs for esophageal neoplasms based on HMDD v2.0. We further prioritize candidate miRNAs for breast neoplasms based on HMDD v1.0. The local LOOCV results and performance analysis of the case study all showed that the proposed model has good and stable performance.https://www.frontiersin.org/articles/10.3389/fgene.2021.720327/fullmicroRNAdiseaseassociation predictiondegreebiased random walk with restart |
| spellingShingle | Jia Qu Chun-Chun Wang Shu-Bin Cai Wen-Di Zhao Xiao-Long Cheng Zhong Ming Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction Frontiers in Genetics microRNA disease association prediction degree biased random walk with restart |
| title | Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction |
| title_full | Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction |
| title_fullStr | Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction |
| title_full_unstemmed | Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction |
| title_short | Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction |
| title_sort | biased random walk with restart on multilayer heterogeneous networks for mirna disease association prediction |
| topic | microRNA disease association prediction degree biased random walk with restart |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.720327/full |
| work_keys_str_mv | AT jiaqu biasedrandomwalkwithrestartonmultilayerheterogeneousnetworksformirnadiseaseassociationprediction AT chunchunwang biasedrandomwalkwithrestartonmultilayerheterogeneousnetworksformirnadiseaseassociationprediction AT shubincai biasedrandomwalkwithrestartonmultilayerheterogeneousnetworksformirnadiseaseassociationprediction AT wendizhao biasedrandomwalkwithrestartonmultilayerheterogeneousnetworksformirnadiseaseassociationprediction AT xiaolongcheng biasedrandomwalkwithrestartonmultilayerheterogeneousnetworksformirnadiseaseassociationprediction AT zhongming biasedrandomwalkwithrestartonmultilayerheterogeneousnetworksformirnadiseaseassociationprediction |