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.
Highlights
In recent years, the knowledge of the role of RNA in gene regulation has emerged from the advances in next-generation sequencing technologies, allowing a deeper and more comprehensive study on full transcriptomes of organisms
We introduce the technique of graph convolution into the model of autoencoder for building an end-to-end deep learning prediction model called GCLMI for inferring new long noncoding RNA (lncRNA)-miRNA interaction on a large scale
Increasing evidence show that lncRNA and miRNA collaborate to form a regulation network for gene regulation
Summary
The knowledge of the role of RNA in gene regulation has emerged from the advances in next-generation sequencing technologies, allowing a deeper and more comprehensive study on full transcriptomes of organisms. It is demonstrated by the ENCODE project that in mammals noncoding RNA could constitute a substantial majority of transcripts within the genome (Science, 2004). There is as much as 98% of the whole human genome encoding for noncoding transcripts, most of which are processed to generate small noncoding RNA such as miRNA, or long noncoding RNA (lncRNA). The aberrant expression level of noncoding RNAs appears to be one of the initiating factors of different types of disease including cancers (Lewis et al, 2003; Calin and Croce, 2006)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
