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MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation

BACKGROUND: Studies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more atten...

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Autores principales: Ma, Yingjun, He, Tingting, Ge, Leixin, Zhang, Chenhao, Jiang, Xingpeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927119/
https://www.ncbi.nlm.nih.gov/pubmed/31865912
http://dx.doi.org/10.1186/s12920-019-0622-4
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author Ma, Yingjun
He, Tingting
Ge, Leixin
Zhang, Chenhao
Jiang, Xingpeng
author_facet Ma, Yingjun
He, Tingting
Ge, Leixin
Zhang, Chenhao
Jiang, Xingpeng
author_sort Ma, Yingjun
collection PubMed
description BACKGROUND: Studies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods. RESULTS: In this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.93795、0.86363、0.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions. CONCLUSIONS: Our KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research.
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spelling pubmed-69271192019-12-30 MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation Ma, Yingjun He, Tingting Ge, Leixin Zhang, Chenhao Jiang, Xingpeng BMC Med Genomics Research BACKGROUND: Studies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods. RESULTS: In this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.93795、0.86363、0.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions. CONCLUSIONS: Our KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research. BioMed Central 2019-12-23 /pmc/articles/PMC6927119/ /pubmed/31865912 http://dx.doi.org/10.1186/s12920-019-0622-4 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Ma, Yingjun
He, Tingting
Ge, Leixin
Zhang, Chenhao
Jiang, Xingpeng
MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation
title MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation
title_full MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation
title_fullStr MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation
title_full_unstemmed MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation
title_short MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation
title_sort mirna-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927119/
https://www.ncbi.nlm.nih.gov/pubmed/31865912
http://dx.doi.org/10.1186/s12920-019-0622-4
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AT zhangchenhao mirnadiseaseinteractionpredictionbasedonkernelneighborhoodsimilarityandmultinetworkbidirectionalpropagation
AT jiangxingpeng mirnadiseaseinteractionpredictionbasedonkernelneighborhoodsimilarityandmultinetworkbidirectionalpropagation