title: Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations.
abstract: MicroRNAs (miRNAs) play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC) of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes) showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
authors: Hongbo
authors: H
authors: Shi
authors: Department of Cardiology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150001, PR China.
authors: Guangde
authors: G
authors: Zhang
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
authors: Meng
authors: M
authors: Zhou
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
authors: Liang
authors: L
authors: Cheng
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
authors: Haixiu
authors: H
authors: Yang
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
authors: Jing
authors: J
authors: Wang
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
authors: Jie
authors: J
authors: Sun
authors: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, PR China.
authors: Zhenzhen
authors: Z
authors: Wang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D008957:Models, Genetic; D010641:Phenotype; D060066:Protein Interaction Maps / Q000235:genetics; D012372:ROC Curve; D011897:Random Allocation; D015203:Reproducibility of Results
chemical_list: D035683:MicroRNAs