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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doaj-d45ad8b25b2c487e84a6588fa999a1f32021-03-30T03:07:02ZengIEEEIEEE Access2169-35362020-01-018165171652710.1109/ACCESS.2019.29580558926470IMDAILM: Inferring miRNA-Disease Association by Integrating lncRNA and miRNA DataYuhua Yao0https://orcid.org/0000-0003-4811-646XBinbin Ji1https://orcid.org/0000-0002-6719-9574Sihong Shi2https://orcid.org/0000-0002-1335-3732Junlin Xu3https://orcid.org/0000-0003-1057-1504Xiaofang Xiao4https://orcid.org/0000-0003-3676-1744Enchao Yu5https://orcid.org/0000-0002-9832-7516Bo Liao6https://orcid.org/0000-0001-7391-4063Jialiang Yang7https://orcid.org/0000-0003-4689-8672School of Mathematics and Statistics, Hainan Normal University, Haikou, ChinaSchool of Mathematics and Statistics, Hainan Normal University, Haikou, ChinaSchool of Mathematics and Statistics, Hainan Normal University, Haikou, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaGeneis Beijing Company, Ltd., Beijing, ChinaSchool of Mathematics and Statistics, Hainan Normal University, Haikou, ChinaSchool of Mathematics and Statistics, Hainan Normal University, Haikou, ChinaIn 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.https://ieeexplore.ieee.org/document/8926470/MiRNAlncRNAmiRNA-disease associationlncRNA-miRNA associationlow-rank matrix completionalternating gradient descent method |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuhua Yao Binbin Ji Sihong Shi Junlin Xu Xiaofang Xiao Enchao Yu Bo Liao Jialiang Yang |
spellingShingle |
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 IEEE Access MiRNA lncRNA miRNA-disease association lncRNA-miRNA association low-rank matrix completion alternating gradient descent method |
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 |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
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. |
topic |
MiRNA lncRNA miRNA-disease association lncRNA-miRNA association low-rank matrix completion alternating gradient descent method |
url |
https://ieeexplore.ieee.org/document/8926470/ |
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