ArticleScientific reports2018
Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 43 citations in OpenAlex.
- SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.Journal of cellular and molecular medicine · 2024Article
- Article
- ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.Briefings in bioinformatics · 2024Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix.Interdisciplinary sciences, computational life sciences · 2023Article
- Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.BMC bioinformatics · 2022Article
- Identification of MiRNA-Disease Associations Based on Information of Multi-Module and Meta-Path.Molecules (Basel, Switzerland) · 2022Article
- Pan-Cancer Analysis of BUB1B/hsa-miR-130a-3p Axis and Identification of Circulating hsa-miR-130a-3p as a Potential Biomarker for Cancer Risk Assessment.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Combined embedding model for MiRNA-disease association prediction.BMC bioinformatics · 2021Article
- Prediction of miRNA-Disease Association Using Deep Collaborative Filtering.BioMed research international · 2021Article
- A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.PloS one · 2021Article
- Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.BMC bioinformatics · 2020Article
- LDAI-ISPS: LncRNA-Disease Associations Inference Based on Integrated Space Projection Scores.International journal of molecular sciences · 2020Article
- MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association.Frontiers in genetics · 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
- Article
- Prediction of Disease-related microRNAs through Integrating Attributes of microRNA Nodes and Multiple Kinds of Connecting Edges.Molecules (Basel, Switzerland) · 2019Article
- Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.International journal of molecular sciences · 2019Article
- Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
microRNAs (miRNAs) mutation and maladjustment are related to the occurrence and development of human diseases. Studies on disease-associated miRNA have contributed to disease diagnosis and treatment. To address the problems, such as low prediction accuracy and failure to predict the relationship between new miRNAs and diseases and so on, we design a Laplacian score of graphs to calculate the global similarity of networks and propose a Global Similarity method based on a Two-tier Random Walk for the prediction of miRNA-disease association (GSTRW) to reveal the correlation between miRNAs and diseases. This method is a global approach that can simultaneously predict the correlation between all diseases and miRNAs in the absence of negative samples. Experimental results reveal that this method is better than existing approaches in terms of overall prediction accuracy and ability to predict orphan diseases and novel miRNAs. A case study on GSTRW for breast cancer and conlon cancer is also conducted, and the majority of miRNA-disease association can be verified by our experiment. This study indicates that this method is feasible and effective.
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