MODIFIED WEIGHTED SIMILARITY IN HETEROGENEOUS GRAPH FOR PREDICTION OF miRNA DISEASE ASSOCIATION

In the last decade lot of experimental research has witnessed and verified the important roles of miRNA in the development of complex human diseases. Publicly available MiRNA data and different analysis methodologies have given rise to the development of many computational models to predict miRNA disease association. Predicting accurate miRNA disease association is very essential for the proper diagnosis and treatment of diseases. During past few years lots of computational methods have been developed. However, each method has its own limitation and it has not yet been possible to develop an efficient method that can predict miRNA-disease associations accurately. In this paper, weighted meta-graph based computational approach for predicting the association between diseases and miRNAs is proposed. The proposed algorithm is designed by integrating available miRNA functional similarity, miRNA similarity based on Environmental factors, miRNA similarity based on diseases to get the average miRNA similarity, disease semantic similarity and disease functional similarity are also integrated to get the average disease similarity. AUC of 0.9617833 on global LOOCV has been achieved using the proposed method.