Identification and Analysis of Human Microbe-Disease Associations by Matrix Decomposition and Label Propagation.

Jia Qu,Yan Zhao,Jun Yin

doi:10.3389/fmicb.2019.00291

Abstract

Studies have shown that microbes exist widely in the human body and are closely related to human complex diseases. Predicting potential associations between microbes and diseases is conducive to understanding the mechanisms of complex diseases and can also facilitate the diagnosis and prevention of human diseases. In this paper, we put forward the Matrix Decomposition and Label Propagation for Human Microbe-Disease Association prediction (MDLPHMDA) on the basis of the dataset of known microbe-disease associations collected from the database of HMDAD and the Gaussian interaction profile kernel similarity for diseases and microbes, disease symptom similarity. Moreover, the performance of our model was evaluated by means of leave-one-out cross validation and five-fold cross validation, and the corresponding AUCs of 0.9034 and 0.8954 ± 0.0030 were gained, respectively. In case studies, 10, 9, 9, and 8 out of the top 10 predicted microbes for asthma, colorectal carcinoma, liver cirrhosis, and type 1 diabetes were confirmed by literatures, respectively. Overall, evaluation results showed that MDLPHMDA has good performance in potential microbe-diseasepositive free parameter, which associations prediction.

Highlights

Microbes are microscopic organisms that may exist in single-celled form or in a colony of cells (Madigan and Michaelt, 2015)
By combining known microbe-disease associations collected from Human Microbe-Disease Association Database (HMDAD), disease symptom similarity and Gaussian interaction profile kernel similarity for microbes and diseases, we introduced a computational model of Matrix Decomposition and Label Propagation for the Human Microbe-Disease Association prediction (MDLPHMDA)
In order to test the prediction performance of MDLPHMDA based on the 450 confirmed microbe-disease associations collected from HMDAD (Ma et al, 2017), our model was compared with two classic algorithms (LRLSHMDA and KATZHMDA) on the basis of the evaluation method of leave-one-out cross validation (LOOCV) and five-fold cross validation

Summary

Introduction

Microbes are microscopic organisms that may exist in single-celled form or in a colony of cells (Madigan and Michaelt, 2015). They live in almost all the habitats from the poles to the deep sea and make up the microbiota in all multicellular organisms (Delong and Pace, 2001). There are trillions of microbes in the human body. Lots of them are beneficial for human health, while others may cause infectious diseases (Thiele et al, 2013). It is accepted that most of the microbes are not intrinsically harmful. The pathogenic microorganisms and the imbalance of resident microbes are closely related to human disease

Methods

Results

Conclusion