A heterogeneous label propagation approach to explore the potential associations between miRNA and disease
Abstract Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex disea...
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doaj-25f4473bcef64acbadfc90720d36f8fd2020-11-25T01:53:41ZengBMCJournal of Translational Medicine1479-58762018-12-0116111410.1186/s12967-018-1722-1A heterogeneous label propagation approach to explore the potential associations between miRNA and diseaseXing Chen0De-Hong Zhang1Zhu-Hong You2School of Information and Control Engineering, China University of Mining and TechnologySchool of Information and Control Engineering, China University of Mining and TechnologyXinjiang Technical Institute of Physics and Chemistry, Chinese Academy of ScienceAbstract Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. Methods In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. Results HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. Conclusions All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.http://link.springer.com/article/10.1186/s12967-018-1722-1miRNADiseasemiRNA-disease associationMulti-networkLabel propagation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xing Chen De-Hong Zhang Zhu-Hong You |
spellingShingle |
Xing Chen De-Hong Zhang Zhu-Hong You A heterogeneous label propagation approach to explore the potential associations between miRNA and disease Journal of Translational Medicine miRNA Disease miRNA-disease association Multi-network Label propagation |
author_facet |
Xing Chen De-Hong Zhang Zhu-Hong You |
author_sort |
Xing Chen |
title |
A heterogeneous label propagation approach to explore the potential associations between miRNA and disease |
title_short |
A heterogeneous label propagation approach to explore the potential associations between miRNA and disease |
title_full |
A heterogeneous label propagation approach to explore the potential associations between miRNA and disease |
title_fullStr |
A heterogeneous label propagation approach to explore the potential associations between miRNA and disease |
title_full_unstemmed |
A heterogeneous label propagation approach to explore the potential associations between miRNA and disease |
title_sort |
heterogeneous label propagation approach to explore the potential associations between mirna and disease |
publisher |
BMC |
series |
Journal of Translational Medicine |
issn |
1479-5876 |
publishDate |
2018-12-01 |
description |
Abstract Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. Methods In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. Results HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. Conclusions All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers. |
topic |
miRNA Disease miRNA-disease association Multi-network Label propagation |
url |
http://link.springer.com/article/10.1186/s12967-018-1722-1 |
work_keys_str_mv |
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