Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

12 Scopus Citations
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Author(s)

  • Yu-An Huang
  • Zhu-Hong You
  • Zexuan Zhu
  • Wen-Zhun Huang
  • Jian-Xin Guo
  • Chang-Qing Yu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number758
Journal / PublicationFrontiers in Genetics
Volume10
Online published29 Aug 2019
Publication statusPublished - Aug 2019

Link(s)

Abstract

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.

Research Area(s)

  • computational prediction model, graph convolution network, LncRNA–miRNA interactions, regulation network, system biology model

Citation Format(s)

Predicting lncRNA-miRNA Interaction via Graph Convolution Auto-Encoder. / Huang, Yu-An; Huang, Zhi-An; You, Zhu-Hong; Zhu, Zexuan; Huang, Wen-Zhun; Guo, Jian-Xin; Yu, Chang-Qing.

In: Frontiers in Genetics, Vol. 10, 758, 08.2019.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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