Affiliation:
1. School of Computer Science, Shaanxi Normal University, Xi’an, Shaanxi 710119, China
Abstract
A large number of clinical observations have showed that metabolites are involved in a variety of important human diseases in the recent years. Nonetheless, the inherent noise and incompleteness in the existing biological datasets are tough factors which limit the prediction accuracy of current computational methods. To solve this problem, in this paper, a prediction method, IBNPLNSMDA, is proposed which uses the improved bipartite network projection method to predict latent metabolite-disease associations based on linear neighborhood similarity. Specifically, liner neighborhood similarity matrix about metabolites (diseases) is reconstructed according to the new feature which is gained by the known metabolite-disease associations and relevant integrated similarities. The improved bipartite network projection method is adopted to infer the potential associations between metabolites and diseases. At last, IBNPLNSMDA achieves a reliable performance in LOOCV (AUC of 0.9634) outperforming the compared methods. In addition, in case studies of four common human diseases, simulation results confirm the utility of our method in discovering latent metabolite-disease pairs. Thus, we believe that IBNPLNSMDA could serve as a reliable computational tool for metabolite-disease associations prediction.
Funder
National Natural Science Foundation of China
Subject
Multidisciplinary,General Computer Science
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献