ArticleMolecular therapy. Nucleic acids2019
Prediction of Potential Disease-Associated MicroRNAs by Using Neural Networks.
Article in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- MIFNDRA: an innovative knowledge-enhanced multimodal fusion and graph learning framework for predicting drug resistance-related ncRNAs.Briefings in bioinformatics · 2026Article
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.Medical oncology (Northwood, London, England) · 2024Review
- Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery.Journal of chemical information and modeling · 2024Article
- Ndufa4 Regulates the Proliferation and Apoptosis of Neurons via miR-145a-5p/Homer1/Ccnd2.Molecular neurobiology · 2023Article
- ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network.BMC genomics · 2023Article
- DRESIS: the first comprehensive landscape of drug resistance information.Nucleic acids research · 2023Article
- BLNIMDA: identifying miRNA-disease associations based on weighted bi-level network.BMC genomics · 2022Article
- A message passing framework with multiple data integration for miRNA-disease association prediction.Scientific reports · 2022Article
- Article
- Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.BMC bioinformatics · 2022Article
- Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism.Biomolecules · 2022Article
- Predict potential miRNA-disease associations based on bounded nuclear norm regularization.Frontiers in genetics · 2022Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Combined embedding model for MiRNA-disease association prediction.BMC bioinformatics · 2021Article
- RFPR-IDP: reduce the false positive rates for intrinsically disordered protein and region prediction by incorporating both fully ordered proteins and disordered proteins.Briefings in bioinformatics · 2021Article
- Prediction of miRNA-Disease Association Using Deep Collaborative Filtering.BioMed research international · 2021Article
- Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.BMC bioinformatics · 2020Article
- NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information.BMC bioinformatics · 2020Article
- Drug repositioning based on the target microRNAs using bilateral-inductive matrix completion.Molecular genetics and genomics : MGG · 2020Article
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Authors and funding
5 authors.
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Abstract
Identifying disease-related microRNAs (miRNAs) is an essential but challenging task in bioinformatics research. Much effort has been devoted to discovering the underlying associations between miRNAs and diseases. However, most studies mainly focus on designing advanced methods to improve prediction accuracy while neglecting to investigate the link predictability of the relationships between miRNAs and diseases. In this work, we construct a heterogeneous network by integrating neighborhood information in the neural network to predict potential associations between miRNAs and diseases, which also consider the imbalance of datasets. We also employ a new computational method called a neural network model for miRNA-disease association prediction (NNMDA). This model predicts miRNA-disease associations by integrating multiple biological data resources. Comparison of our work with other algorithms reveals the reliable performance of NNMDA. Its average AUC score was 0.937 over 15 diseases in a 5-fold cross-validation and AUC of 0.8439 based on leave-one-out cross-validation. The results indicate that NNMDA could be used in evaluating the accuracy of miRNA-disease associations. Moreover, NNMDA was applied to two common human diseases in two types of case studies. In the first type, 26 out of the top 30 predicted miRNAs of lung neoplasms were confirmed by the experiments. In the second type of case study for new diseases without any known miRNAs related to it, we selected breast neoplasms as the test example by hiding the association information between the miRNAs and this disease. The results verified 50 out of the top 50 predicted breast-neoplasm-related miRNAs.
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