AUTHOR=Chen Min , Zhang Yi , Li Ang , Li Zejun , Liu Wenhua , Chen Zheng 

TITLE=Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction

JOURNAL=Frontiers in Genetics

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2019.00385

DOI=10.3389/fgene.2019.00385

ISSN=1664-8021

ABSTRACT=In recent years, miRNA variation and dysregulation have been found to be closely related to human tumours，and identifying miRNA–disease associations is helpful for understanding the mechanisms of disease or tumour development and is greatly significant for the prognosis, diagnosis and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on Co-Neighbour to predict miRNA–disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbours is proposed, and the co-neighbours were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbours, we utilised the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross-validation (LOOCV) on different data sets. The AUC value of BHCN on the gold benchmark data set was 0.7973, and the AUC obtained on the prediction data set was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease and dbDEMC, with accuracy rates of 96% and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100% and 96%, respectively, thereby demonstrating the favourable predictive power of BHCN for potentially relevant miRNAs.