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- W2805950880 abstract "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." @default.
- W2805950880 created "2018-06-13" @default.
- W2805950880 creator A5031510017 @default.
- W2805950880 creator A5047968661 @default.
- W2805950880 creator A5072059330 @default.
- W2805950880 date "2018-04-24" @default.
- W2805950880 modified "2023-10-16" @default.
- W2805950880 title "Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA–Disease Association" @default.
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- W2805950880 doi "https://doi.org/10.1038/s41598-018-24532-7" @default.
- W2805950880 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5915491" @default.
- W2805950880 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29691434" @default.
- W2805950880 hasPublicationYear "2018" @default.
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