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The measurement of human miRNA functional similarity is an important research for studying miRNA-related therapeutic strategy. Pair wise-based approaches using disease-miRNA associations have recently become a popular tool for inferring miRNA functional similarity. However, the miRNA functional similarity is vitally influenced by calculation of the disease semantic similarity in those methods. Moreover, integrating information content with hierarchical structure can improve calculation of the miRNA functional similarity. Therefore, we propose a group-wise method for inferring the miRNA functional similarity, named GMFS. First, the information content is computed by using disease MeSH descriptors to describe the specific of disease. Second, the acquirement of disease feature is based on the hierarchical structure as well as the information content of disease. Finally, the miRNA functional similarity is measured by using both miRNA-disease associations and the disease feature. To validate the effectiveness of the GMFS, we compare our method with several existing methods in terms of the average similarity of intra-family, inter-family, intra-cluster, and inter-cluster groups. The $p$ -values achieved by non-parametric test further indicate that the GMFS could have reliable miRNA similarity. Besides, the correlation between other biological information of the miRNA and the miRNA functional similarity is analyzed. The influence of the varying parameter is shown. We also demonstrate that the constructed network based on the miRNA functional similarity is a scale-free and small-world network. The superior performance on uncovering lymphoma-related miRNAs explains the ability of the GMFS inferring the miRNA functional similarity.

A Novel Group Wise-Based Method for Calculating Human miRNA Functional Similarity