ArticleJournal of cellular and molecular medicine2018
GIMDA: Graphlet interaction-based MiRNA-disease association prediction.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- RSCMDA: Prediction of Potential miRNA-Disease Associations Based on a Robust Similarity Constraint Learning Method.Interdisciplinary sciences, computational life sciences · 2021Article
- Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.BMC medical genomics · 2021Article
- Expression levels and clinical values of miR-92b-3p in breast cancer.World journal of surgical oncology · 2021Article
- MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.BMC medical informatics and decision making · 2021Article
- MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection.Frontiers in genetics · 2020Article
- BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction.Frontiers in genetics · 2020Article
- Benchmark of computational methods for predicting microRNA-disease associations.Genome biology · 2019Article
- Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction.Molecular therapy. Nucleic acids · 2019Article
- Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.PLoS computational 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
- Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction.Frontiers in genetics · 2019Article
- FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association.BMC genomics · 2018Article
- A novel semi-supervised model for miRNA-disease association prediction based onJournal of translational medicine · 2018Article
- A novel information diffusion method based on network consistency for identifying disease related microRNAs.RSC advances · 2018Article
- SNMDA: A novel method for predicting microRNA-disease associations based on sparse neighbourhood.Journal of cellular and molecular medicine · 2018Article
- Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association.Scientific reports · 2018Article
- Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization.Frontiers in genetics · 2018Article
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4 authors.
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Abstract
MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures.
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