ArticlePLoS computational biology2019
Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.
Article in PLoS computational biology, 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.
- Genome-phenome association prediction using weighted deep matrix factorization with a multisource graph attention network.Quantitative biology (Beijing, China) · 2026Article
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- PathoRM: Computational inference of pathogenic RNA methylation sites by incorporating multi-view features.PLoS computational biology · 2025Article
- Adaptive multi-view learning method for enhanced drug repurposing using chemical-induced transcriptional profiles, knowledge graphs, and large language models.Journal of pharmaceutical analysis · 2025Article
- SPLHRNMTF: robust orthogonal non-negative matrix tri-factorization with self-paced learning and dual hypergraph regularization for predicting miRNA-disease associations.BMC genomics · 2024Article
- MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.Briefings in bioinformatics · 2024Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.BMC bioinformatics · 2022Article
- Predicting miRNA-disease associations based on graph attention network with multi-source information.BMC bioinformatics · 2022Article
- Impact of Air Pollution in Airway Diseases: Role of the Epithelial Cells (Cell Models and Biomarkers).International journal of molecular sciences · 2022Review
- Machine learning in the development of targeting microRNAs in human disease.Frontiers in genetics · 2022Review
- Predict potential miRNA-disease associations based on bounded nuclear norm regularization.Frontiers in genetics · 2022Article
- Predicting Multiple Types of Associations Between miRNAs and Diseases Based on Graph Regularized Weighted Tensor Decomposition.Frontiers in bioengineering and biotechnology · 2022Article
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
- GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.PLoS computational biology · 2021Article
- Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.BMC medical genomics · 2021Article
- Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities.Scientific reports · 2021Article
- DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding.iScience · 2021Article
- GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction.PLoS computational biology · 2021Article
- Combined embedding model for MiRNA-disease association prediction.BMC bioinformatics · 2021Article
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3 authors.
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Abstract
Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations, they suffer from various limitations that affect the prediction accuracy and their applicability. In this study, we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning(AMVML). Specifically, considering the inherent noise existed in the current dataset, we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles. We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning. In particular, we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate. To comprehensively illustrate the prediction performance of our method, we compared AMVML with four state-of-the-art methods under different validation frameworks. As a result, our method achieved comparable performance under various evaluation metrics, which suggests that our method is capable of discovering greater number of true miRNA-disease associations. The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker. Together, the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs.
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