ArticleFrontiers in genetics2019
Predicting lncRNA-miRNA Interaction
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Bioinformatics Approaches in Noncoding RNAs Research.Experientia supplementum (2012) · 2026Review
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- lncRNA TUG1 and kidney diseases.BMC nephrology · 2025Review
- Predicting noncoding RNA and disease associations using multigraph contrastive learning.Scientific reports · 2025Article
- Graph convolutional neural networks improved target-specific scoring functions for cGAS and kRAS in virtual screening.Computational and structural biotechnology journal · 2025Article
- Computational prediction of new therapeutic effects of probiotics.Scientific reports · 2024Article
- Role of miR-424 in the carcinogenesis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Review
- LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms.iScience · 2023Article
- PmliHFM: Predicting Plant miRNA-lncRNA Interactions with Hybrid Feature Mining Network.Interdisciplinary sciences, computational life sciences · 2023Article
- BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA-miRNA interaction prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences.BMC genomics · 2022Article
- Classification of Breast Cancer and Breast Neoplasm Scenarios Based on Machine Learning and Sequence Features from lncRNAs-miRNAs-Diseases Associations.Interdisciplinary sciences, computational life sciences · 2021Article
- Ensemble Deep Learning Based on Multi-level Information Enhancement and Greedy Fuzzy Decision for Plant miRNA-lncRNA Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2021Article
- Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions.International journal of molecular sciences · 2021Review
- Overview of host miRNA properties and their association with epigenetics, long non-coding RNAs, and Xeno-infectious factors.Cell & bioscience · 2021Review
- Functional Interaction among lncRNA HOTAIR and MicroRNAs in Cancer and Other Human Diseases.Cancers · 2021Review
- Graph Neural Networks and Their Current Applications in Bioinformatics.Frontiers in genetics · 2021Article
- A Compressive Review about TaxolMolecules (Basel, Switzerland) · 2020Review
- Application of deep learning in genomics.Science China. Life sciences · 2020Review
- Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives.Non-coding RNA · 2020Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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