ArticleScientific reports2016
Prediction of miRNA-disease associations with a vector space model.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed.
- Uncovering miRNA-Disease Associations Through Graph Based Neural Network Representations.Biomedicines · 2026Article
- DAEMDA: A Method with Dual-Channel Attention Encoding for miRNA-Disease Association Prediction.Biomolecules · 2023Article
- GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization.BMC genomics · 2023Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- MiRNA-Seq reveals key MicroRNAs involved in fat metabolism of sheep liver.Frontiers in genetics · 2023Article
- MEAHNE: miRNA-Disease Association Prediction Based on Semantic Information in a Heterogeneous Network.Life (Basel, Switzerland) · 2022Article
- Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.BMC bioinformatics · 2022Article
- Identification of MiRNA-Disease Associations Based on Information of Multi-Module and Meta-Path.Molecules (Basel, Switzerland) · 2022Article
- Hierarchical graph attention network for miRNA-disease association prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- Inferring human miRNA-disease associations via multiple kernel fusion on GCNII.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
- Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases.Frontiers in genetics · 2022Article
- RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method.Interdisciplinary sciences, computational life sciences · 2021Article
- Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities.Scientific reports · 2021Article
- A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network.Scientific reports · 2021Article
- Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA-Disease Association Prediction.Frontiers in genetics · 2021Article
- Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.BMC bioinformatics · 2020Article
- FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.Molecular genetics and genomics : MGG · 2020Article
- PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences.BMC bioinformatics · 2020Article
- A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression.Frontiers in bioengineering and biotechnology · 2020Article
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2 authors.
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Abstract
MicroRNAs play critical roles in many physiological processes. Their dysregulations are also closely related to the development and progression of various human diseases, including cancer. Therefore, identifying new microRNAs that are associated with diseases contributes to a better understanding of pathogenicity mechanisms. MicroRNAs also represent a tremendous opportunity in biotechnology for early diagnosis. To date, several in silico methods have been developed to address the issue of microRNA-disease association prediction. However, these methods have various limitations. In this study, we investigate the hypothesis that information attached to miRNAs and diseases can be revealed by distributional semantics. Our basic approach is to represent distributional information on miRNAs and diseases in a high-dimensional vector space and to define associations between miRNAs and diseases in terms of their vector similarity. Cross validations performed on a dataset of known miRNA-disease associations demonstrate the excellent performance of our method. Moreover, the case study focused on breast cancer confirms the ability of our method to discover new disease-miRNA associations and to identify putative false associations reported in databases.
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