Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases

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ID: 302559
2010
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Abstract
Abstract Motivation: It is popular to explore meaningful molecular targets and infer new functions of genes through gene functional similarity measuring and gene functional network construction. However, little work is available in this field for microRNA (miRNA) genes due to limited miRNA functional annotations. With the rapid accumulation of miRNAs, it is increasingly needed to uncover their functional relationships in a systems level. Results: It is known that genes with similar functions are often associated with similar diseases, and the relationship of different diseases can be represented by a structure of directed acyclic graph (DAG). This is also true for miRNA genes. Therefore, it is feasible to infer miRNA functional similarity by measuring the similarity of their associated disease DAG. Based on the above observations and the rapidly accumulated human miRNA-disease association data, we presented a method to infer the pairwise functional similarity and functional network for human miRNAs based on the structures of their disease relationships. Comparisons showed that the calculated miRNA functional similarity is well associated with prior knowledge of miRNA functional relationship. More importantly, this method can also be used to predict novel miRNA biomarkers and to infer novel potential functions or associated diseases for miRNAs. In addition, this method can be easily extended to other species when sufficient miRNA-associated disease data are available for specific species. Availability: The online tool is available at http://cmbi.bjmu.edu.cn/misim Contact: cuiqinghua@hsc.pku.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W2141222510 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Dong Wang, Juan Wang, Ming Lü, Fei Song, Qinghua Cui
Journal BMC Bioinformatics
Year 2010
DOI
10.1093/bioinformatics/btq241
URL
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