A novel bi-directional heterogeneous network selection method for disease and microbial association prediction

Abstract

Microorganisms in the human body have a great impact on human health. Therefore, mastering the potential relationship between microorganisms and diseases is helpful to understand the pathogenesis of diseases and is of great significance to the prevention, diagnosis, and treatment of diseases. In order to predict the potential microbial disease relationship, we propose a new computational model. Firstly, a bi-directional heterogeneous microbial disease network is constructed by integrating multiple similarities, including Gaussian kernel similarity, microbial function similarity, disease semantic similarity, and disease symptom similarity. Secondly, the neighbor information of the network is learned by random walk; Finally, the selection model is used for information aggregation, and the microbial disease node pair is analyzed. Our method is superior to the existing methods in leave-one-out cross-validation and five-fold cross-validation. Moreover, in case studies of different diseases, our method was proven to be effective.

Keywords: Bi-directional heterogeneous network; Potential microorganism disease prediction; Random walk; causal selection model.

Fig. 1

Overall flowchart of BDHNS. Step1: We fuse the calculated similarity of two microorganisms with the similarity of four diseases. Step2:We first build two one-way heterogeneous network respectively corresponding microorganisms and diseases, and then convert two one-way heterogeneous networks into a two-way heterogeneous network of microorganisms and diseases. Step3:We use the enhanced random walk and selection algorithm to predict the potential association between microorganisms and diseases

Fig. 2

Heterogeneous map

Fig. 3

Select algorithm graph

Fig. 4

ROC curve of five methods in 5-fold cross validation

Fig. 5

ROC curve of five methods in Loocv