Abstract
Microbes are vital in human health. It is helpful to promote diagnostic and treatment of human disease and drug development by identifying microbe-disease associations. However, knowledge in this area still needs to be further improved. In this paper, a new computational model using matrix completion to predict human microbe-disease associations (mHMDA, Fig. 1) is developed. First, we extract the disease feature by Gaussian kernel-based similarity and symptom-based similarity. Meanwhile, the microbe feature is computed by Gaussian kernel-based similarity. As treating potential association as the missing elements of a matrix, the matrix completion is adopted to get the potential microbe-disease associations. Leave-one-out cross-validation (LOOCV) is carried out which get the AUC (The area under ROC curve) of 0.928 showing the effectiveness of mHMDA. Furthermore, 5-fold CV get the AUCs of 0.8838 ± 0.0044 (mean ± standard deviation). Moreover, through the four case studies (asthma, inflammatory bowel disease (IBD), type 2 diabetes (T2D), and type 1 diabetes (T1D)), we find that nine, ten, nine, and eight of top-ten inferred microorganisms for the four diseases are previously verified by experiments. All these results indicate the effectiveness of mHMDA. mHMDA might be helpful to infer the disease-related microorganisms.
Highlights
Microorganisms are very important to human health [1], [2]
It will be helpful to explore the pathogenesis of diseases and prevent or treat diseases by studying the interactions of microbes and diseases (MD)
The results show that mHMDA is effective to identify disease-related microorganisms
Summary
Microorganisms are very important to human health [1], [2]. Numerous lab experiments and clinic studies have found novel links between human diseases and microbes.It will be helpful to explore the pathogenesis of diseases and prevent or treat diseases by studying the interactions of microbes and diseases (MD). Microorganisms are very important to human health [1], [2]. Numerous lab experiments and clinic studies have found novel links between human diseases and microbes. It will be helpful to explore the pathogenesis of diseases and prevent or treat diseases by studying the interactions of microbes and diseases (MD). Many computational methods were developed to exploit new relationship between microorganisms and diseases. HMDAD (human microbe-disease associations database) provides basic knowledge of the MD associations [3]. With the knowledge of this database, many models were proposed. KATZHMDA adopted KATZ method to find potential MD associations [5]. PBHMDA was proposed to treat associations as links between microbes and diseases by searching depth firstly algorithm [6]. Some researchers used the matrix factorization technique to investigate the association, such as CMFHMDA utilizing factorization of the collab-
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