Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations

Abstract Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and t...

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Autores principales: Feng Zhou, Meng-Meng Yin, Cui-Na Jiao, Zhen Cui, Jing-Xiu Zhao, Jin-Xing Liu
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Lenguaje:EN
Publicado: BMC 2021
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Acceso en línea:https://doaj.org/article/06795340c3f240aa8b3c389c1767cb02
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spelling oai:doaj.org-article:06795340c3f240aa8b3c389c1767cb022021-11-28T12:11:16ZBipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations10.1186/s12859-021-04486-w1471-2105https://doaj.org/article/06795340c3f240aa8b3c389c1767cb022021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04486-whttps://doaj.org/toc/1471-2105Abstract Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. Results By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. Conclusions Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.Feng ZhouMeng-Meng YinCui-Na JiaoZhen CuiJing-Xiu ZhaoJin-Xing LiuBMCarticleMiRNA–disease associations association predictionMatrix factorizationBipartite graphGaussian interaction profileComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-16 (2021)
institution DOAJ
collection DOAJ
language EN
topic MiRNA–disease associations association prediction
Matrix factorization
Bipartite graph
Gaussian interaction profile
Computer applications to medicine. Medical informatics
R858-859.7
Biology (General)
QH301-705.5
spellingShingle MiRNA–disease associations association prediction
Matrix factorization
Bipartite graph
Gaussian interaction profile
Computer applications to medicine. Medical informatics
R858-859.7
Biology (General)
QH301-705.5
Feng Zhou
Meng-Meng Yin
Cui-Na Jiao
Zhen Cui
Jing-Xiu Zhao
Jin-Xing Liu
Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations
description Abstract Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. Results By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. Conclusions Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.
format article
author Feng Zhou
Meng-Meng Yin
Cui-Na Jiao
Zhen Cui
Jing-Xiu Zhao
Jin-Xing Liu
author_facet Feng Zhou
Meng-Meng Yin
Cui-Na Jiao
Zhen Cui
Jing-Xiu Zhao
Jin-Xing Liu
author_sort Feng Zhou
title Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations
title_short Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations
title_full Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations
title_fullStr Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations
title_full_unstemmed Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations
title_sort bipartite graph-based collaborative matrix factorization method for predicting mirna-disease associations
publisher BMC
publishDate 2021
url https://doaj.org/article/06795340c3f240aa8b3c389c1767cb02
work_keys_str_mv AT fengzhou bipartitegraphbasedcollaborativematrixfactorizationmethodforpredictingmirnadiseaseassociations
AT mengmengyin bipartitegraphbasedcollaborativematrixfactorizationmethodforpredictingmirnadiseaseassociations
AT cuinajiao bipartitegraphbasedcollaborativematrixfactorizationmethodforpredictingmirnadiseaseassociations
AT zhencui bipartitegraphbasedcollaborativematrixfactorizationmethodforpredictingmirnadiseaseassociations
AT jingxiuzhao bipartitegraphbasedcollaborativematrixfactorizationmethodforpredictingmirnadiseaseassociations
AT jinxingliu bipartitegraphbasedcollaborativematrixfactorizationmethodforpredictingmirnadiseaseassociations
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