IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data

In recent years, more and more studies have shown that microRNAs (miRNAs) play a key role in many important biological processes. Dysregulation of miRNAs can lead to a variety of diseases like cancers, thus predicting potential miRNA-disease associations is important for understanding drug developme...

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Autores principales:	Yuhua Yao, Binbin Ji, Sihong Shi, Junlin Xu, Xiaofang Xiao, Enchao Yu, Bo Liao, Jialiang Yang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	MiRNA lncRNA miRNA-disease association lncRNA-miRNA association low-rank matrix completion alternating gradient descent method Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/d45ad8b25b2c487e84a6588fa999a1f3
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spelling	oai:doaj.org-article:d45ad8b25b2c487e84a6588fa999a1f32021-11-18T00:00:45ZIMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data2169-353610.1109/ACCESS.2019.2958055https://doaj.org/article/d45ad8b25b2c487e84a6588fa999a1f32020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8926470/https://doaj.org/toc/2169-3536In recent years, more and more studies have shown that microRNAs (miRNAs) play a key role in many important biological processes. Dysregulation of miRNAs can lead to a variety of diseases like cancers, thus predicting potential miRNA-disease associations is important for understanding drug development and disease pathogenesis, diagnosis and treatment. It is known that experimental methods to validate miRNA-disease associations typically involve miRNA knockout or knockdown, which is time and labor-intensive. As a result, computational models have been developed to predict unknown miRNA-disease associations from available information related to miRNAs, diseases, genes, and so on. However, their performances are yet to be improved. Noticing that appropriately combining multiple data-source is usually helpful for improving prediction accuracy, we have developed IMDAILM: Inferring miRNA-Disease Association by integrating lncRNA and miRNA data, a low-rank matrix completion model integrating miRNA, long noncoding RNA (lncRNA) and disease information to predict miRNA-disease associations. Specifically, the miRNA-disease association network and the lncRNA-disease association network are fused to form a new heterogeneous network consisting of 3 types of nodes representing miRNAs, lncRNAs and diseases. In addition, a negative sample inference method was proposed to infer unrelated miRNA-disease pairs. Based on both heterogeneous network and negative samples, a low-rank matrix completion model is proposed and solved. In practice, IMDAILM achieved an area under the curve (AUC) of 0.8884 for predicting miRNAs associated with diseases under the 5-fold cross-validation (CV), outperforming a few recent methods. IMDAILM also yielded an AUC of 0.8870 for predicting both lncRNAs and miRNAs associated with diseases. In addition, the 5-fold CV results indicate that IMDAILM is also superior to other methods in predicting miRNAs associated with isolated diseases. Finally, we confirmed a few novel predicted miRNAs associated with specific diseases like lung cancers by literature mining. In summary, the integration of lncRNA information into a matrix completion framework contributes to the prediction of miRNA-disease associations.Yuhua YaoBinbin JiSihong ShiJunlin XuXiaofang XiaoEnchao YuBo LiaoJialiang YangIEEEarticleMiRNAlncRNAmiRNA-disease associationlncRNA-miRNA associationlow-rank matrix completionalternating gradient descent methodElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 16517-16527 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	MiRNA lncRNA miRNA-disease association lncRNA-miRNA association low-rank matrix completion alternating gradient descent method Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	MiRNA lncRNA miRNA-disease association lncRNA-miRNA association low-rank matrix completion alternating gradient descent method Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yuhua Yao Binbin Ji Sihong Shi Junlin Xu Xiaofang Xiao Enchao Yu Bo Liao Jialiang Yang IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data
description	In recent years, more and more studies have shown that microRNAs (miRNAs) play a key role in many important biological processes. Dysregulation of miRNAs can lead to a variety of diseases like cancers, thus predicting potential miRNA-disease associations is important for understanding drug development and disease pathogenesis, diagnosis and treatment. It is known that experimental methods to validate miRNA-disease associations typically involve miRNA knockout or knockdown, which is time and labor-intensive. As a result, computational models have been developed to predict unknown miRNA-disease associations from available information related to miRNAs, diseases, genes, and so on. However, their performances are yet to be improved. Noticing that appropriately combining multiple data-source is usually helpful for improving prediction accuracy, we have developed IMDAILM: Inferring miRNA-Disease Association by integrating lncRNA and miRNA data, a low-rank matrix completion model integrating miRNA, long noncoding RNA (lncRNA) and disease information to predict miRNA-disease associations. Specifically, the miRNA-disease association network and the lncRNA-disease association network are fused to form a new heterogeneous network consisting of 3 types of nodes representing miRNAs, lncRNAs and diseases. In addition, a negative sample inference method was proposed to infer unrelated miRNA-disease pairs. Based on both heterogeneous network and negative samples, a low-rank matrix completion model is proposed and solved. In practice, IMDAILM achieved an area under the curve (AUC) of 0.8884 for predicting miRNAs associated with diseases under the 5-fold cross-validation (CV), outperforming a few recent methods. IMDAILM also yielded an AUC of 0.8870 for predicting both lncRNAs and miRNAs associated with diseases. In addition, the 5-fold CV results indicate that IMDAILM is also superior to other methods in predicting miRNAs associated with isolated diseases. Finally, we confirmed a few novel predicted miRNAs associated with specific diseases like lung cancers by literature mining. In summary, the integration of lncRNA information into a matrix completion framework contributes to the prediction of miRNA-disease associations.
format	article
author	Yuhua Yao Binbin Ji Sihong Shi Junlin Xu Xiaofang Xiao Enchao Yu Bo Liao Jialiang Yang
author_facet	Yuhua Yao Binbin Ji Sihong Shi Junlin Xu Xiaofang Xiao Enchao Yu Bo Liao Jialiang Yang
author_sort	Yuhua Yao
title	IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data
title_short	IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data
title_full	IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data
title_fullStr	IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data
title_full_unstemmed	IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data
title_sort	imdailm: inferring mirna-disease association by integrating lncrna and mirna data
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/d45ad8b25b2c487e84a6588fa999a1f3
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IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA Data

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