Predicting Metabolite-Disease Associations Based on Linear Neighborhood Similarity with Improved Bipartite Network Projection Algorithm

Xiujuan Lei,Cheng Zhang

doi:10.1155/2020/9342640

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.

Highlights

Metabolites, the final products of cellular regulatory process, whose levels can be considered as the ultimate response of biological systems to genetic or environmental changes have significant effects in human body [1]
According to the results of the Leave-one-out across validation (LOOCV), Area under the curve (AUC) which is the area under the ROC curve containing true-positive rate (TPR) and the falsepositive rate (FPR) and AUPR which is the area under PR curve containing precision and recall plays significant roles in evaluation performance of method
After LOOCV, IBNPLNSMDA obtains reliable AUC value of 0.9634 and AUPR value of 0.4971 which indicates that IBNPLNSMDA has satisfactory prediction performances

Summary

Introduction

Metabolites, the final products of cellular regulatory process, whose levels can be considered as the ultimate response of biological systems to genetic or environmental changes have significant effects in human body [1] It is a trend for disease researches to find the effect in molecular level with the rapidly developing biomedical instruments, and analytical platforms [2, 3] and metabolisms disrupted by disease state are widely identified as disease signatures [4]. RWRMDA [6] is the first method to explore the latent associations between metabolites and diseases, which pushes the development of computational method in metabolomics They do not consider the diseases similarity when calculating the last predicted results.

Methods

Results

Conclusion