ArticleOncotarget2017
PRMDA: personalized recommendation-based MiRNA-disease association prediction.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering.BMC medical informatics and decision making · 2021Article
- A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network.Scientific reports · 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
- Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model.Scientific reports · 2020Article
- Benchmark of computational methods for predicting microRNA-disease associations.Genome biology · 2019Article
- LncRNA-Disease Association Prediction Using Two-Side Sparse Self-Representation.Frontiers in genetics · 2019Article
- FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association.BMC genomics · 2018Article
- MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.PLoS computational biology · 2018Article
- Prediction of microRNA-disease associations based on distance correlation set.BMC bioinformatics · 2018Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, researchers have been increasingly focusing on microRNAs (miRNAs) with accumulating evidence indicating that miRNAs serve as a vital role in various biological processes and dysfunctions of miRNAs are closely related with human complex diseases. Predicting potential associations between miRNAs and diseases is attached considerable significance in the domains of biology, medicine, and bioinformatics. In this study, we developed a computational model of Personalized Recommendation-based MiRNA-Disease Association prediction (PRMDA) to predict potential related miRNA for all diseases by implementing personalized recommendation-based algorithm based on integrated similarity for diseases and miRNAs. PRMDA is a global method capable of prioritizing candidate miRNAs for all diseases simultaneously. Moreover, the model could be applied to diseases without any known associated miRNAs. PRMDA obtained AUC of 0.8315 based on leave-one-out cross validation, which demonstrated that PRMDA could be regarded as a reliable tool for miRNA-disease association prediction. Besides, we implemented PRMDA on the HMDD V1.0 and HMDD V2.0 databases for three kinds of case studies about five important human cancers in order to test the performance of the model from different perspectives. As a result, 92%, 94%, 88%, 96% and 88% out of the top 50 candidate miRNAs predicted by PRMDA for Colon Neoplasms, Esophageal Neoplasms, Lymphoma, Lung Neoplasms and Breast Neoplasms, respectively, were confirmed by experimental reports.
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