ArticleJournal of translational medicine2018
A heterogeneous label propagation approach to explore the potential associations between miRNA and disease.
Article in Journal of translational medicine, 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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19 citing papers in PubMed.
- Genome-phenome association prediction using weighted deep matrix factorization with a multisource graph attention network.Quantitative biology (Beijing, China) · 2026Article
- A message passing framework with multiple data integration for miRNA-disease association prediction.Scientific reports · 2022Article
- SMMDA: Predicting miRNA-Disease Associations by Incorporating Multiple Similarity Profiles and a Novel Disease Representation.Biology · 2022Article
- Inferring human miRNA-disease associations via multiple kernel fusion on GCNII.Frontiers in genetics · 2022Article
- GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.PLoS computational biology · 2021Article
- DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.Molecular therapy : the journal of the American Society of Gene Therapy · 2021Article
- WVMDA: Predicting miRNA-Disease Association Based on Weighted Voting.Frontiers in genetics · 2021Article
- ILPMDA: Predicting miRNA-Disease Association Based on Improved Label Propagation.Frontiers in genetics · 2021Article
- A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.PloS one · 2021Article
- Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.BMC bioinformatics · 2020Article
- RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network.BMC bioinformatics · 2020Article
- Graph regularized LBMC bioinformatics · 2020Article
- 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
- Article
- Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method.Scientific reports · 2019Article
- Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction.Frontiers in genetics · 2019Article
- Construction and Analysis of Molecular Association Network by Combining Behavior Representation and Node Attributes.Frontiers in genetics · 2019Article
- A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases.International journal of molecular sciences · 2018Article
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3 authors.
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Abstract
backgroundResearch on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments.
methodsIn this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA-miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction.
resultsHLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports.
conclusionsAll the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers.
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