A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network

Zou, Shuai; Zhang, Jingpu; Zhang, Zuping

doi:10.1371/journal.pone.0184394

Cited by 62 publications

(36 citation statements)

References 62 publications

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“…KATZHMD 14 , RW3RHMDA 18 and BiRWHMDA 19 are state-of-the-art methods for predicting microbe-disease associations. All these methods are based on a heterogeneous network which was constructed by connecting the microbe similarity network and the disease similarity network via the known microbe-disease associations.…”

Section: Resultsmentioning

confidence: 99%

“…Based on traditional random walk with three parameters on the heterogeneous network constructed by Spearman correlation, Shen et al derived a method (RWRHMDA) for prioritization of candidate microbes to predict disease-microbe association 18 . Zou et al developed a computational model (BiRWHMDA) to predict potential microbe-disease associations by bi-random walk on the heterogeneous network 19 . It is anticipated that various computational prediction models could improve the identification of novel microbe-disease association.…”

Section: Introductionmentioning

confidence: 99%

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

Wu¹,

Gao²,

Zhang³

et al. 2018

Int. J. Biol. Sci.

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wu¹,

Gao²,

Zhang³

et al. 2018

Int. J. Biol. Sci.

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“…We compare LGRSH with three methods including LRLSHMDA (Wang et al, 2017), KATZHMDA (Chen et al, 2017) and BiRWHMDA (Zou et al, 2017). These four methods are measured by Precision-recall curve.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Li et al (2019) constructed a bidirectional weighted network by combining a normalized Gaussian interaction scheme with a bidirectional recommendation model. Zou et al (2017) used a bi-random walk and logistic function transformation on a heterogeneous network constructed based on the GIP kernel similarity. Through a combination of the GIP kernel similarity and LapRLS classification, Wang et al (2017) designed a computing model LRLSHMDA, which is semisupervised .…”

Section: Introductionmentioning

confidence: 99%

Predicting Microbe-Disease Association by Learning Graph Representations and Rule-Based Inference on the Heterogeneous Network

Lei

Wang

2020

Front. Microbiol.

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More and more clinical observations have implied that microbes have great effects on human diseases. Understanding the relations between microbes and diseases are of profound significance for disease prevention and therapy. In this paper, we propose a predictive model based on the known microbe-disease associations to discover potential microbe-disease associations through integrating Learning Graph Representations and a modified Scoring mechanism on the Heterogeneous network (called LGRSH). Firstly, the similarity networks for microbe and disease are obtained based on the similarity of Gaussian interaction profile kernel. Then, we construct a heterogeneous network including these two similarity networks and microbe-disease associations' network. After that, the embedding algorithm Node2vec is implemented to learn representations of nodes in the heterogeneous network. Finally, according to these low-dimensional vector representations, we calculate the relevance between each microbe and disease by utilizing a modified rule-based inference method. By comparison with three other methods including LRLSHMDA, KATZHMDA and BiRWHMDA, LGRSH performs better than others. Moreover, in case studies of asthma, Chronic Obstructive Pulmonary Disease and Inflammatory Bowel Disease, there are 8, 8, and 10 out of the top-10 discovered disease-related microbes were validated respectively, demonstrating that LGRSH performs well in predicting potential microbe-disease associations.

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“…It includes internal and external validations. Generally, the leave-one-out (LOO) cross-validation technology is often considered as the most economical and popular internal validation to evaluate the predictive ability of the model [ 30 ]. LOO cross-validation involves using one object from the dataset as the validation set, and the remaining dataset serves as the training data.…”

Section: Methodsmentioning

confidence: 99%

Identfication of Potent LXRβ-Selective Agonists without LXRα Activation by In Silico Approaches

et al. 2018

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Activating Liver X receptors (LXRs) represents a promising therapeutic option for dyslipidemia. However, activating LXRα may cause undesired lipogenic effects. Discovery of highly LXRβ-selective agonists without LXRα activation were indispensable for dyslipidemia. In this study, in silico approaches were applied to develop highly potent LXRβ-selective agonists based on a series of newly reported 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione-based LXRα/β dual agonists. Initially, Kohonen and stepwise multiple linear regression SW-MLR were performed to construct models for LXRβ agonists and LXRα agonists based on the structural characteristics of LXRα/β dual agonists, respectively. The obtained LXRβ agonist model gave a good predictive ability (R2train = 0.837, R2test = 0.843, Q2LOO = 0.715), and the LXRα agonist model produced even better predictive ability (R2train = 0.968, R2test = 0.914, Q2LOO = 0.895). Also, the two QSAR models were independent and can well distinguish LXRβ and LXRα activity. Then, compounds in the ZINC database met the lower limit of structural similarity of 0.7, compared to the 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione scaffold subjected to our QSAR models, which resulted in the discovery of ZINC55084484 with an LXRβ prediction value of pEC50 equal to 7.343 and LXRα prediction value of pEC50 equal to −1.901. Consequently, nine newly designed compounds were proposed as highly LXRβ-selective agonists based on ZINC55084484 and molecular docking, of which LXRβ prediction values almost exceeded 8 and LXRα prediction values were below 0.

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A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network

Cited by 62 publications

References 62 publications

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

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

Predicting Microbe-Disease Association by Learning Graph Representations and Rule-Based Inference on the Heterogeneous Network

Identfication of Potent LXRβ-Selective Agonists without LXRα Activation by In Silico Approaches

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