ArticleBMC genomics2015
DISMIRA: Prioritization of disease candidates in miRNA-disease associations based on maximum weighted matching inference model and motif-based analysis.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Specific MicroRNAs Found in Extracellular Matrix Vesicles Regulate Proliferation and Differentiation in Growth Plate Chondrocytes.Calcified tissue international · 2021Article
- RWRMTN: a tool for predicting disease-associated microRNAs based on a microRNA-target gene network.BMC bioinformatics · 2020Article
- MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection.Frontiers in genetics · 2020Article
- LSGSP: a novel miRNA-disease association prediction model using a Laplacian score of the graphs and space projection federated method.RSC advances · 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 information diffusion method based on network consistency for identifying disease related microRNAs.RSC advances · 2018Article
- Determining causal miRNAs and their signaling cascade in diseases using an influence diffusion model.Scientific reports · 2017Article
- miRsig: a consensus-based network inference methodology to identify pan-cancer miRNA-miRNA interaction signatures.Scientific reports · 2017Article
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10 authors.
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Abstract
backgroundMicroRNAs (miRNAs) have increasingly been found to regulate diseases at a significant level. The interaction of miRNA and diseases is a complex web of multilevel interactions, given the fact that a miRNA regulates upto 50 or more diseases and miRNAs/diseases work in clusters. The clear patterns of miRNA regulations in a disease are still elusive.
methodsIn this work, we approach the miRNA-disease interactions from a network scientific perspective and devise two approaches - maximum weighted matching model (a graph theoretical algorithm which provides the result by solving an optimization equation of selecting the most prominent set of diseases) and motif-based analyses (which investigates the motifs of the miRNA-disease network and selects the most prominent set of diseases based on their maximum number of participation in motifs, thereby revealing the miRNA-disease interaction dynamics) to determine and prioritize the set of diseases which are most certainly impacted upon the activation of a group of queried miRNAs, in a miRNA-disease network. RESULTS AND
conclusionOur tool, DISMIRA implements the above mentioned approaches and presents an interactive visualization which helps the user in exploring the networking dynamics of miRNAs and diseases by analyzing their neighbors, paths and topological features. A set of miRNAs can be used in this analysis to get the associated diseases for the input group of miRs with ranks and also further analysis can be done to find key miRs or diseases, shortest paths etc. DISMIRA can be accessed online for free at http://bnet.egr.vcu.edu:8080/dismira.
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