ArticleFrontiers in physiology2018
GRMDA: Graph Regression for MiRNA-Disease Association Prediction.
Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Hessian RegularizedInterdisciplinary sciences, computational life sciences · 2024Article
- LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier.Interdisciplinary sciences, computational life sciences · 2023Article
- JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA.Scientific reports · 2022Article
- BLNIMDA: identifying miRNA-disease associations based on weighted bi-level network.BMC genomics · 2022Article
- RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation.Molecular genetics and genomics : MGG · 2021Review
- SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost.BMC bioinformatics · 2021Article
- MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.BMC medical informatics and decision making · 2021Article
- A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.PloS one · 2021Article
- MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation.BMC medical genomics · 2019Article
- SKF-LDA: Similarity Kernel Fusion for Predicting lncRNA-Disease Association.Molecular therapy. Nucleic acids · 2019Article
- Benchmark of computational methods for predicting microRNA-disease associations.Genome biology · 2019Article
- WBNPMD: weighted bipartite network projection for microRNA-disease association prediction.Journal of translational medicine · 2019Article
- A novel information diffusion method based on network consistency for identifying disease related microRNAs.RSC advances · 2018Article
- Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association.Scientific reports · 2018Article
- A Novel Computational Method for the Identification of Potential miRNA-Disease Association Based on Symmetric Non-negative Matrix Factorization and Kronecker Regularized Least Square.Frontiers in genetics · 2018Article
- Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization.Frontiers in genetics · 2018Article
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4 authors.
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Abstract
Nowadays, as more and more associations between microRNAs (miRNAs) and diseases have been discovered, miRNA has gradually become a hot topic in the biological field. Because of the high consumption of time and money on carrying out biological experiments, computational method which can help scientists choose the most likely associations between miRNAs and diseases for further experimental studies is desperately needed. In this study, we proposed a method of Graph Regression for MiRNA-Disease Association prediction (GRMDA) which combines known miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. We used Gaussian interaction profile kernel similarity to supplement the shortage of miRNA functional similarity and disease semantic similarity. Furthermore, the graph regression was synchronously performed in three latent spaces, including association space, miRNA similarity space, and disease similarity space, by using two matrix factorization approaches called Singular Value Decomposition and Partial Least-Squares to extract important related attributes and filter the noise. In the leave-one-out cross validation and five-fold cross validation, GRMDA obtained the AUCs of 0.8272 and 0.8080 ± 0.0024, respectively. Thus, its performance is better than some previous models. In the case study of Lymphoma using the recorded miRNA-disease associations in HMDD V2.0 database, 88% of top 50 predicted miRNAs were verified by experimental literatures. In order to test the performance of GRMDA on new diseases with no known related miRNAs, we took Breast Neoplasms as an example by regarding all the known related miRNAs as unknown ones. We found that 100% of top 50 predicted miRNAs were verified. Moreover, 84% of top 50 predicted miRNAs in case study for Esophageal Neoplasms based on HMDD V1.0 were verified to have known associations. In conclusion, GRMDA is an effective and practical method for miRNA-disease association prediction.
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