Original Research ARTICLE
Predicting lncRNA-miRNA interaction via graph convolution auto-encoder
- 1City University of Hong Kong, Hong Kong
- 2Xijing University, China
- 3Shenzhen University, China
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 no 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.
Keywords: lncRNA-miRNA interaction, graph convolution network, computational prediction model, Regulation network, System biology models
Received: 28 Jan 2019;
Accepted: 17 Jul 2019.
Copyright: © 2019 Huang, Huang, You, Zhu, Yu, Huang and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Prof. Zhu-hong You, Xijing University, Xi'an, Shaanxi Province, China, firstname.lastname@example.org
Prof. Zexuan Zhu, Shenzhen University, Shenzhen, 518060, Guangdong Province, China, email@example.com