A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction.

Lei Wang,Xiang Feng,Dawei Yuan,Hao Li,Jialiang Yang,Yuqi Wang

doi:10.3389/fmicb.2019.00684

Abstract

A growing number of clinical observations have indicated that microbes are involved in a variety of important human diseases. It is obvious that in-depth investigation of correlations between microbes and diseases will benefit the prevention, early diagnosis, and prognosis of diseases greatly. Hence, in this paper, based on known microbe-disease associations, a prediction model called NBLPIHMDA was proposed to infer potential microbe-disease associations. Specifically, two kinds of networks including the disease similarity network and the microbe similarity network were first constructed based on the Gaussian interaction profile kernel similarity. The bidirectional label propagation was then applied on these two kinds of networks to predict potential microbe-disease associations. We applied NBLPIHMDA on Human Microbe-Disease Association database (HMDAD), and compared it with 3 other recent published methods including LRLSHMDA, BiRWMP, and KATZHMDA based on the leave-one-out cross validation and 5-fold cross validation, respectively. As a result, the area under the receiver operating characteristic curves (AUCs) achieved by NBLPIHMDA were 0.8777 and 0.8958 ± 0.0027, respectively, outperforming the compared methods. In addition, in case studies of asthma, colorectal carcinoma, and Chronic obstructive pulmonary disease, simulation results illustrated that there are 10, 10, and 8 out of the top 10 predicted microbes having been confirmed by published documentary evidences, which further demonstrated that NBLPIHMDA is promising in predicting novel associations between diseases and microbes as well.

Highlights

With the development of sequencing technologies, studies on microbes in soils, oceans, human bodies and other places have received increasing attention from the scientific community (Methé et al, 2012)
We have proposed a novel computational model called NBLPIHMDA based on the bidirectional label propagation to predict potential microbe-disease associations
In the framework of leave-one-out cross validation (LOOCV), each known microbe-disease association was taken as the test sample in turn, while the remaining known associations were taken as the training set

Summary

Introduction

With the development of sequencing technologies, studies on microbes in soils, oceans, human bodies and other places have received increasing attention from the scientific community (Methé et al, 2012). I.e., the collection of microbes existing in human tissues and biological fluids, includes various species such as archaea, eukaryotes, bacteria, and viruses. The NBLPIHMDA vast majority of microbes are harmless to human body, some of which are even indispensable for our metabolism, growth, and development. There are parasitic microbes involving in the physiological mechanisms of the human body and playing a vital role in the process of energy acquisition and storage, salvage of energy, and nutrient, resistance to pathogens and foreign microorganisms, immune responses, and other metabolic processes (Guarner and Malagelada, 2003). Human health will be greatly affected by the human microbiota, and the disorder and imbalance of them will sometimes lead to diseases

Methods

Results

Conclusion