Human Microbe-Disease Association Prediction Based on Adaptive Boosting.

Li-Hong Peng,Ming-Xi Liu,Jun Yin,Yan Zhao,Liqian Zhou

doi:10.3389/fmicb.2018.02440

Abstract

There are countless microbes in the human body, and they play various roles in the physiological process. There is growing evidence that microbes are closely associated with human diseases. Researching disease-related microbes helps us understand the mechanisms of diseases and provides new strategies for diseases diagnosis and treatment. Many computational models have been proposed to predict disease-related microbes, in this paper, we developed a model of Adaptive Boosting for Human Microbe-Disease Association prediction (ABHMDA) to reveal the associations between diseases and microbes by calculating the relation probability of disease-microbe pair using a strong classifier. Our model could be applied to new diseases without any known related microbes. In order to assess the prediction power of the model, global and local leave-one-out cross validation (LOOCV) were implemented. As shown in the results, the global and local LOOCV values reached 0.8869 and 0.7910, respectively. What’s more, 10, 10, and 8 out of the top 10 microbes predicted to be most likely to be associated with Asthma, Colorectal carcinoma and Type 1 diabetes were all verified by relevant literatures or database HMDAD, respectively. The above results verify the superior predictive performance of ABHMDA.

Highlights

For the sake of revealing the association between microbe-diseases better, in this paper, we proposed a model of Adaptive Boosting for Human Microbe-Disease Association prediction (ABHMDA) to uncover the associations between diseases and microbes by calculating the relation probability of disease-microbe pair using a strong classifier
We used ABHMDA to conduct case studies on three diseases. 10, 10, and 8 out of the top 10 microbes predicted to be most likely to be associated with Asthma, Colorectal carcinoma and Type 1 diabetes were all verified by relevant literatures or database Human Microbe-Disease Association Database (HMDAD), respectively
We could plot the Receiver operating characteristics (ROC) curve, which was composed of points corresponding to different thresholds, we could obtain the Area under the ROC curve (AUC)

Summary

Introduction

Microbes could be divided into the following types: bacteria, fungi, viruses, archaea, protozoa, and so on (Sommer and Backhed, 2013). There are a number of microbes living in the human tissues, such as gut (Grenham et al, 2011), skin (Fredricks, 2001) and lung (Cole, 1989). There are studies showing that microorganisms are involved in many biological processes in the human body, such as metabolic function, immune function, and so on (Gill et al, 2006). It is not surprising that there are links between microbes and human diseases (Consortium, 2012).Some researchers had found a close relationship between human type 2 diabetes and changes in the composition of the intestinal microbiota (Larsen et al, 2010). Gut microbes could induce colorectal cancer by generating butyrate that

Methods

Results

Conclusion