Research And Application For Association Between Diseases And MicroRNA

In the field of bioinformatics, the association between the disease and genetic is an extremely important research direction. Increasing evidence has revealed that microRNAs (miRNAs) play more and more important roles in the development and progression of human diseases. However, efforts made to uncover OMIM disease-miRNA associations are lacking and the majority of diseases in the OMIM database are not associated with any miRNA.Therefore, there is a strong incentitve to develop computational methods to detect potential OMIM disease-miRNA associations.In this thesis, random walk on OMIM disease similarity network is applied to predict potential OMIM disease-miRNA associations under the assumption that functionally related miRNAs are often associated with phenotypically similar diseases.Our method makes full use of global disease similarity values. Simultaneously this thesis improved the random walk algorithm, build a node importance ranking matrix, made the convergence rate of this algorithm became faster, improve accuracy.We tested our method on1226known OMIM disease-miRNA assosications in the framework of level-one-out cross-validation and achieved an area under the ROC curve of73.57%. Excellent performance enables us to predict a numberof new potential OMIM disease-RNA associations and the newly prediceted associations are publicly released to facilitate future studies. Some predicted associations with high ranks were manully checked and were confirmed from the publicly available databases, which wasa strong evidence for the practical relevance of our method.