ArticleCells2019
A Novel Computational Model for Predicting microRNA-Disease Associations Based on Heterogeneous Graph Convolutional Networks.
Article in Cells, 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.
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- Review
- Disentangled similarity graph attention heterogeneous biological memory network for predicting disease-associated miRNAs.BMC genomics · 2024Article
- An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model.Journal of cheminformatics · 2024Article
- From biological data to oscillator models using SINDy.iScience · 2024Article
- SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.Briefings in bioinformatics · 2024Article
- Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning.BMC cancer · 2024Article
- ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.Briefings in bioinformatics · 2024Article
- Review
- DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis.Nucleic acids research · 2023Article
- MTAGCN: predicting miRNA-target associations in Camellia sinensis var. assamica through graph convolution neural network.BMC bioinformatics · 2022Article
- Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases.Frontiers in genetics · 2022Article
- Microblog-HAN: A micro-blog rumor detection model based on heterogeneous graph attention network.PloS one · 2022Article
- An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing.Frontiers in pharmacology · 2022Article
- GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.PLoS computational biology · 2021Article
- Heterogeneous Types of miRNA-Disease Associations Stratified by Multi-Layer Network Embedding and Prediction.Biomedicines · 2021Article
- MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network.Frontiers in pharmacology · 2021Article
- Graph Neural Networks and Their Current Applications in Bioinformatics.Frontiers in genetics · 2021Article
- Article
- FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.Molecular genetics and genomics : MGG · 2020Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying the interactions between disease and microRNA (miRNA) can accelerate drugs development, individualized diagnosis, and treatment for various human diseases. However, experimental methods are time-consuming and costly. So computational approaches to predict latent miRNA-disease interactions are eliciting increased attention. But most previous studies have mainly focused on designing complicated similarity-based methods to predict latent interactions between miRNAs and diseases. In this study, we propose a novel computational model, termed heterogeneous graph convolutional network for miRNA-disease associations (HGCNMDA), which is based on known human protein-protein interaction (PPI) and integrates four biological networks: miRNA-disease, miRNA-gene, disease-gene, and PPI network. HGCNMDA achieved reliable performance using leave-one-out cross-validation (LOOCV). HGCNMDA is then compared to three state-of-the-art algorithms based on five-fold cross-validation. HGCNMDA achieves an AUC of 0.9626 and an average precision of 0.9660, respectively, which is ahead of other competitive algorithms. We further analyze the top-10 unknown interactions between miRNA and disease. In summary, HGCNMDA is a useful computational model for predicting miRNA-disease interactions.
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