ArticleBMC bioinformatics2019
NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Prediction of miRNA-disease associations based on PCA and cascade forest.BMC bioinformatics · 2024Article
- BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix.Interdisciplinary sciences, computational life sciences · 2023Article
- iLncDA-RSN: identification of lncRNA-disease associations based on reliable similarity networks.Frontiers in genetics · 2023Article
- Article
- Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations.BMC bioinformatics · 2021Article
- Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities.Scientific reports · 2021Article
- SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.PLoS computational biology · 2021Article
- IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier.BMC bioinformatics · 2021Article
- MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations.BMC bioinformatics · 2020Article
- RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network.BMC bioinformatics · 2020Article
- A random forest based computational model for predicting novel lncRNA-disease associations.BMC bioinformatics · 2020Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- Multiview Consensus Graph Learning for lncRNA-Disease Association Prediction.Frontiers in genetics · 2020Article
- RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations.BMC bioinformatics · 2019Article
- Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis.Frontiers in genetics · 2019Review
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Authors and funding
5 authors.
Funding
Abstract
backgroundPredicting meaningful miRNA-disease associations (MDAs) is costly. Therefore, an increasing number of researchers are beginning to focus on methods to predict potential MDAs. Thus, prediction methods with improved accuracy are under development. An efficient computational method is proposed to be crucial for predicting novel MDAs. For improved experimental productivity, large biological datasets are used by researchers. Although there are many effective and feasible methods to predict potential MDAs, the possibility remains that these methods are flawed.
resultsA simple and effective method, known as Nearest Profile-based Collaborative Matrix Factorization (NPCMF), is proposed to identify novel MDAs. The nearest profile is introduced to our method to achieve the highest AUC value compared with other advanced methods. For some miRNAs and diseases without any association, we use the nearest neighbour information to complete the prediction.
conclusionsTo evaluate the performance of our method, five-fold cross-validation is used to calculate the AUC value. At the same time, three disease cases, gastric neoplasms, rectal neoplasms and colonic neoplasms, are used to predict novel MDAs on a gold-standard dataset. We predict the vast majority of known MDAs and some novel MDAs. Finally, the prediction accuracy of our method is determined to be better than that of other existing methods. Thus, the proposed prediction model can obtain reliable experimental results.
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