ArticleJournal of translational medicine2017
MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.
Article in Journal of translational medicine, 2017. 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 biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- RMDGCN: Prediction of RNA methylation and disease associations based on graph convolutional network with attention mechanism.PLoS computational biology · 2023Article
- HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA-Disease Association Prediction.International journal of molecular sciences · 2022Article
- Inferring human miRNA-disease associations via multiple kernel fusion on GCNII.Frontiers in genetics · 2022Article
- Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.BMC medical genomics · 2021Article
- RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations.BMC bioinformatics · 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
- LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method.RSC advances · 2019Article
- NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations.BMC bioinformatics · 2019Article
- MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation.Journal of cellular and molecular medicine · 2019Article
- RWHMDA: Random Walk on Hypergraph for Microbe-Disease Association Prediction.Frontiers in microbiology · 2019Article
- Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction.Frontiers in genetics · 2019Article
- FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association.BMC genomics · 2018Article
- A novel semi-supervised model for miRNA-disease association prediction based onJournal of translational medicine · 2018Article
- A novel information diffusion method based on network consistency for identifying disease related microRNAs.RSC advances · 2018Article
- SNMDA: A novel method for predicting microRNA-disease associations based on sparse neighbourhood.Journal of cellular and molecular medicine · 2018Article
- Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association.Scientific reports · 2018Article
- LLCMDA: A Novel Method for Predicting miRNA Gene and Disease Relationship Based on Locality-Constrained Linear Coding.Frontiers in genetics · 2018Article
- GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods.RNA biology · 2018Article
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4 authors.
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Abstract
backgroundRecently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity.
methodsIn this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA.
resultsMKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively.
conclusionsAll the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.
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