PRWHMDA: Human Microbe-Disease Association Prediction by Random Walk on the Heterogeneous Network with PSO

Abstract

Microorganisms resided in human body play a vital role in metabolism, immune defense, nutrition absorption, cancer control and protection against pathogen colonization. The changes of microbial communities can cause human diseases. Based on the known microbe-disease association, we presented a novel computational model employing Random Walking with Restart optimized by Particle Swarm Optimization (PSO) on the heterogeneous interlinked network of Human Microbe-Disease Associations (PRWHMDA) (see Figure 1). Based on the known human microbe-disease associations, we constructed the heterogeneous interlinked network with Cosine similarity. The extended random walk with restart (RWR) method was derived to get the potential microbe-disease associations. PSO was utilized to get the optimal parameters of RWR. To evaluate the prediction effectiveness, we performed leave one out cross validation (LOOCV) and 5-fold cross validation (CV), which got the AUC (The area under ROC curve) of 0.915 (LOOCV) and the average AUCs of 0.8875 ± 0.0046 (5-fold CV). Moreover, we carried out three case studies of asthma, inflammatory bowel disease (IBD) and type 1 diabetes (T1D) for the further evaluation. The result showed that 10, 10 and 9 of top-10 predicted microbes were verified by previously published experimental results, respectively. It is anticipated that PRWHMDA can be effective to identify the disease-related microbes and maybe helpful to disclose the relationship between microorganisms and their human host.

Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection

“…Inflammatory bowel disease is also known as non-specific enteritis or idiopathic enteritis, whose etiology has not been completely clear. Also, there is no cure for it in medicine currently (Wu et al, 2018). The top 10 microbes most likely to be associated with inflammatory bowel disease were predicted by HMDA_Pred, which was confirmed by relevant literatures, as shown in Table 7.…”

“…In addition, we compared the HMDA-Pred method with the BRWMDA ( Yan et al, 2019 ), PBHMDA ( Huang et al, 2017 ), PRWHMDA ( Wu et al, 2018 ), NGRHMDA ( Huang et al, 2017 ), KATZBNRA ( Li et al, 2019 ), NTSHMDA ( Luo and Long, 2018 ), BMCMDA ( Shi et al, 2018 ), NCPHMDA ( Bao et al, 2017 ), ABHMDA ( Peng et al, 2018 ), NBLPIHMDA ( Wang et al, 2019 ), and GRNMFHMDA ( He et al, 2018 ) methods. As shown in Figure 6 , these AUC values extracted from the original papers include 0.9397, 0.9169, 0.9150, 0.9111, 0.9098, 0.9070, 0.9060, 0.9039, 0.8869, 0.8777, and 0.8715.…”

Human Microbe-Disease Association Prediction Based on Adaptive Boosting

“…Therefore, a deeper study of asthma was imperative, and studies had shown that there was a close relationship between the microbes in the respiratory tract and the development and progression of asthma ( Marri et al, 2013 ). For example, studies had shown that Firmicutes was reduced in asthmatic patients compared with normal humans ( Wu et al, 2018 ). In contrast, Proteobacteria accounted for a larger proportion of microorganisms in asthma patients than normal people ( Marri et al, 2013 ).…”

Challenges in the construction of knowledge bases for human microbiome-disease associations

“…Binary Matrix Completion MDA (BMCMDA) developed by Shi et al [88] is a method that infers new microbe-disease associations from HMDAD developed by Ma et al [19]. Similar work includes KATZHMDA [89], PRWHMDA [90], BiRWHMDA [91], and PBHMDA [92]. However, they share a common weakness that they have been evaluated on limited training data from HMDAD which provides a limited dataset that is far from sufficient as the ground truth.…”