Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder
The interaction of miRNA and lncRNA is known to be important for gene regulations. However, the number of known lncRNA-miRNA interactions is still very limited and there are limited computational tools available for predicting new ones. Considering that lncRNAs and miRNAs share internal patterns in...
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doaj-f9defca29967410cb7c0b67106d19ea82020-11-25T00:04:24ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-08-011010.3389/fgene.2019.00758450735Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-EncoderYu-An Huang0Zhi-An Huang1Zhu-Hong You2Zexuan Zhu3Wen-Zhun Huang4Jian-Xin Guo5Chang-Qing Yu6College of Electronics and Information Engineering, Xijing University, Xi’an, ChinaDepartment of Computer Science, City University of Hong Kong, Kowloon, Hong KongCollege of Electronics and Information Engineering, Xijing University, Xi’an, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, ChinaCollege of Electronics and Information Engineering, Xijing University, Xi’an, ChinaCollege of Electronics and Information Engineering, Xijing University, Xi’an, ChinaCollege of Electronics and Information Engineering, Xijing University, Xi’an, ChinaThe interaction of miRNA and lncRNA is known to be important for gene regulations. However, the number of known lncRNA-miRNA interactions is still very limited and there are limited computational tools available for predicting new ones. Considering that lncRNAs and miRNAs share internal patterns in the partnership between each other, the underlying lncRNA-miRNA interactions could be predicted by utilizing the known ones, which could be considered as a semi-supervised learning problem. It is shown that the attributes of lncRNA and miRNA have a close relationship with the interaction between each other. Effective use of side information could be helpful for improving the performance especially when the training samples are limited. In view of this, we proposed an end-to-end prediction model called GCLMI (Graph Convolution for novel lncRNA-miRNA Interactions) by combining the techniques of graph convolution and auto-encoder. Without any preprocessing process on the feature information, our method can incorporate raw data of node attributes with the topology of the interaction network. Based on a real dataset collected from a public database, the results of experiments conducted on k-fold cross validations illustrate the robustness and effectiveness of the prediction performance of the proposed prediction model. We prove the graph convolution layer as designed in the proposed model able to effectively integrate the input data by filtering the graph with node features. The proposed model is anticipated to yield highly potential lncRNA-miRNA interactions in the scenario that different types of numerical features describing lncRNA or miRNA are provided by users, serving as a useful computational tool.https://www.frontiersin.org/article/10.3389/fgene.2019.00758/fullLncRNA–miRNA interactionsgraph convolution networkcomputational prediction modelregulation networksystem biology model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yu-An Huang Zhi-An Huang Zhu-Hong You Zexuan Zhu Wen-Zhun Huang Jian-Xin Guo Chang-Qing Yu |
spellingShingle |
Yu-An Huang Zhi-An Huang Zhu-Hong You Zexuan Zhu Wen-Zhun Huang Jian-Xin Guo Chang-Qing Yu Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder Frontiers in Genetics LncRNA–miRNA interactions graph convolution network computational prediction model regulation network system biology model |
author_facet |
Yu-An Huang Zhi-An Huang Zhu-Hong You Zexuan Zhu Wen-Zhun Huang Jian-Xin Guo Chang-Qing Yu |
author_sort |
Yu-An Huang |
title |
Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder |
title_short |
Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder |
title_full |
Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder |
title_fullStr |
Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder |
title_full_unstemmed |
Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder |
title_sort |
predicting lncrna-mirna interaction via graph convolution auto-encoder |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2019-08-01 |
description |
The interaction of miRNA and lncRNA is known to be important for gene regulations. However, the number of known lncRNA-miRNA interactions is still very limited and there are limited computational tools available for predicting new ones. Considering that lncRNAs and miRNAs share internal patterns in the partnership between each other, the underlying lncRNA-miRNA interactions could be predicted by utilizing the known ones, which could be considered as a semi-supervised learning problem. It is shown that the attributes of lncRNA and miRNA have a close relationship with the interaction between each other. Effective use of side information could be helpful for improving the performance especially when the training samples are limited. In view of this, we proposed an end-to-end prediction model called GCLMI (Graph Convolution for novel lncRNA-miRNA Interactions) by combining the techniques of graph convolution and auto-encoder. Without any preprocessing process on the feature information, our method can incorporate raw data of node attributes with the topology of the interaction network. Based on a real dataset collected from a public database, the results of experiments conducted on k-fold cross validations illustrate the robustness and effectiveness of the prediction performance of the proposed prediction model. We prove the graph convolution layer as designed in the proposed model able to effectively integrate the input data by filtering the graph with node features. The proposed model is anticipated to yield highly potential lncRNA-miRNA interactions in the scenario that different types of numerical features describing lncRNA or miRNA are provided by users, serving as a useful computational tool. |
topic |
LncRNA–miRNA interactions graph convolution network computational prediction model regulation network system biology model |
url |
https://www.frontiersin.org/article/10.3389/fgene.2019.00758/full |
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