ArticleRNA biology2018
Predicting microRNA-disease associations using bipartite local models and hubness-aware regression.
Article in RNA biology, 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.
- HLGNN-MDA: Heuristic Learning Based on Graph Neural Networks for miRNA-Disease Association Prediction.International journal of molecular sciences · 2022Article
- Article
- TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction.Molecular therapy. Nucleic acids · 2021Article
- MicroRNA-203-3p inhibits the proliferation, invasion and migration of pancreatic cancer cells by downregulating fibroblast growth factor 2.Oncology letters · 2021Article
- A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network.Scientific reports · 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
- SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction.Frontiers in cell and developmental biology · 2021Article
- Graph regularized LBMC 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
- MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection.Frontiers in genetics · 2020Article
- IMPMD: An Integrated Method for Predicting Potential Associations Between miRNAs and Diseases.Current genomics · 2019Article
- miR‑146a‑5p suppresses ATP‑binding cassette subfamily G member 1 dysregulation in patients with refractory Mycoplasma pneumoniae via interleukin 1 receptor‑associated kinase 1 downregulation.International journal of molecular medicine · 2019Article
- Benchmark of computational methods for predicting microRNA-disease associations.Genome biology · 2019Article
- LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method.RSC advances · 2019Article
- Prediction of Potential Disease-Associated MicroRNAs by Using Neural Networks.Molecular therapy. Nucleic acids · 2019Article
- Dual Laplacian regularized matrix completion for microRNA-disease associations prediction.RNA biology · 2019Article
- Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction.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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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The development and progression of numerous complex human diseases have been confirmed to be associated with microRNAs (miRNAs) by various experimental and clinical studies. Predicting potential miRNA-disease associations can help us understand the underlying molecular and cellular mechanisms of diseases and promote the development of disease treatment and diagnosis. Due to the high cost of conventional experimental verification, proposing a new computational method for miRNA-disease association prediction is an efficient and economical way. Since previous computational models ignored the hubness phenomenon, we presented a novel computational model of Bipartite Local models and Hubness-Aware Regression for MiRNA-Disease Association prediction (BLHARMDA). In this method, we first used known miRNA-disease associations to calculate the Jaccard similarity between miRNAs and between diseases, then utilized a modified kNNs model in the bipartite local model method. As a result, we effectively alleviated the detriments from 'bad' hubs. BLHARMDA obtained AUCs of 0.9141 and 0.8390 in the global and local leave-one-out cross validation, respectively, which outperformed most of the previous models and proved high prediction performance of BLHARMDA. Besides, the standard deviation of 0.0006 in 5-fold cross validation confirmed our model's prediction stability and the averaged prediction accuracy of 0.9120 showed the high precision of our model. In addition, to further evaluate our model's accuracy, we implemented BLHARMDA on three typical human diseases in three different types of case studies. As a result, 49 (Esophageal Neoplasms), 50 (Lung Neoplasms) and 50 (Carcinoma Hepatocellular) out of the top 50 related miRNAs were validated by recent experimental discoveries.
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