ArticleJournal of cellular and molecular medicine2018
SNMDA: A novel method for predicting microRNA-disease associations based on sparse neighbourhood.
Article in Journal of cellular and molecular medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- LE-MDCAP: A Computational Model to Prioritize Causal miRNA-Disease Associations.International journal of molecular sciences · 2021Article
- RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method.Interdisciplinary sciences, computational life sciences · 2021Article
- Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction.Frontiers in bioengineering and biotechnology · 2020Article
- Benchmark of computational methods for predicting microRNA-disease associations.Genome biology · 2019Article
- MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation.Journal of cellular and molecular medicine · 2019Article
- SNMDA: A novel method for predicting microRNA-disease associations based on sparse neighbourhood.Journal of cellular and molecular medicine · 2018Article
- LLCMDA: A Novel Method for Predicting miRNA Gene and Disease Relationship Based on Locality-Constrained Linear Coding.Frontiers in genetics · 2018Article
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Authors and funding
5 authors.
Funding
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Abstract
miRNAs are a class of small noncoding RNAs that are associated with a variety of complex biological processes. Increasing studies have shown that miRNAs have close relationships with many human diseases. The prediction of the associations between miRNAs and diseases has thus become a hot topic. Although traditional experimental methods are reliable, they could only identify a limited number of associations as they are time-consuming and expensive. Consequently, great efforts have been made to effectively predict reliable disease-related miRNAs based on computational methods. In this study, we present a novel approach to predict the potential microRNA-disease associations based on sparse neighbourhood. Specifically, our method takes advantage of the sparsity of the miRNA-disease association network and integrates the sparse information into the current similarity matrices for both miRNAs and diseases. To demonstrate the utility of our method, we applied global LOOCV, local LOOCV and five-fold cross-validation to evaluate our method, respectively. The corresponding AUCs are 0.936, 0.882 and 0.934. Three types of case studies on five common diseases further confirm the performance of our method in predicting unknown miRNA-disease associations. Overall, results show that SNMDA can predict the potential associations between miRNAs and diseases effectively.
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Registered trials
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