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
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	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
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
_version_	1718408119919312896

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

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