ArticleBMC bioinformatics2018
Prediction of microRNA-disease associations based on distance correlation set.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Swing Trend Prediction of Main Guide Bearing in Hydropower Units Based on MFS-DCGNN.Sensors (Basel, Switzerland) · 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
- Hierarchical graph attention network for miRNA-disease association prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.BMC medical genomics · 2021Article
- Robust gene coexpression networks using signed distance correlation.Bioinformatics (Oxford, England) · 2021Article
- Combined embedding model for MiRNA-disease association prediction.BMC bioinformatics · 2021Article
- Prediction of miRNA-Disease Association Using Deep Collaborative Filtering.BioMed research international · 2021Article
- MicroRNA-26a inhibits wound healing through decreased keratinocytes migration by regulating ITGA5 through PI3K/AKT signaling pathway.Bioscience reports · 2020Article
- MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association.Frontiers in genetics · 2020Article
- A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations.BMC bioinformatics · 2019Article
- A Novel Network-Based Computational Model for Prediction of Potential LncRNA⁻Disease Association.International journal of molecular sciences · 2019Article
- A Probabilistic Matrix Factorization Method for Identifying lncRNA-disease Associations.Genes · 2019Article
- A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration.Computational and mathematical methods in medicine · 2019Article
- Elevation of miR-191-5p level and its potential signaling pathways in hepatocellular carcinoma: a study validated by microarray and in-house qRT-PCR with 1,291 clinical samples.International journal of clinical and experimental pathology · 2019Article
- A Novel Neighborhood-Based Computational Model for Potential MiRNA-Disease Association Prediction.Computational and mathematical methods in medicine · 2019Article
- A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases.International journal of molecular sciences · 2018Article
- A Novel Probability Model for LncRNA⁻Disease Association Prediction Based on the Naïve Bayesian Classifier.Genes · 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
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundRecently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited.
resultsIn this study, a new computational method based on a distance correlation set is developed to predict miRNA-disease associations (DCSMDA) by integrating known lncRNA-disease associations, known miRNA-lncRNA associations, disease semantic similarity, and various lncRNA and disease similarity measures. The novelty of DCSMDA is due to the construction of a miRNA-lncRNA-disease network, which reveals that DCSMDA can be applied to predict potential lncRNA-disease associations without requiring any known miRNA-disease associations. Although the implementation of DCSMDA does not require known disease-miRNA associations, the area under curve is 0.8155 in the leave-one-out cross validation. Furthermore, DCSMDA was implemented in case studies of prostatic neoplasms, lung neoplasms and leukaemia, and of the top 10 predicted associations, 10, 9 and 9 associations, respectively, were separately verified in other independent studies and biological experimental studies. In addition, 10 of the 10 (100%) associations predicted by DCSMDA were supported by recent bioinformatical studies.
conclusionsAccording to the simulation results, DCSMDA can be a great addition to the biomedical research field.
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