MGATMDA: Predicting Microbe-Disease Associations via Multi-Component Graph Attention Network

Liu, Dayun; Liu, Junyi; Yi, Luo; He, Qianhua; Deng, Lei

doi:10.1109/tcbb.2021.3116318

Cited by 20 publications

(22 citation statements)

References 36 publications

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“…We compared our method with four microbial disease association prediction methods, including BiRHMDA [4], KATZ [3], LRLSNMDA [7], ABHMDA [8], Liu's method [18], MGATMDA [19]. In five-fold cross-validation and leave-one-out cross-validation, all methods use the same data set for training and testing.…”

Section: Compare Other Methodsmentioning

confidence: 99%

“…Liu et al [18] have combined non negative matrix decomposition, random walk and capsule neural network to predict the association between microorganisms and diseases. A method based on multi-component graph attention network (GATMDA) was proposed to predict the potential association between microorganisms and diseases [19]. However, many similarities of various microorganisms (diseases) have not been fully utilized.…”

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confidence: 99%

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A novel bi-directional heterogeneous network selection method for disease and microbial association prediction

et al. 2022

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Microorganisms in the human body have a great impact on human health. Therefore, mastering the potential relationship between microorganisms and diseases is helpful to understand the pathogenesis of diseases and is of great significance to the prevention, diagnosis, and treatment of diseases. In order to predict the potential microbial disease relationship, we propose a new computational model. Firstly, a bi-directional heterogeneous microbial disease network is constructed by integrating multiple similarities, including Gaussian kernel similarity, microbial function similarity, disease semantic similarity, and disease symptom similarity. Secondly, the neighbor information of the network is learned by random walk; Finally, the selection model is used for information aggregation, and the microbial disease node pair is analyzed. Our method is superior to the existing methods in leave-one-out cross-validation and five-fold cross-validation. Moreover, in case studies of different diseases, our method was proven to be effective.

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Section: Compare Other Methodsmentioning

confidence: 99%

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A novel bi-directional heterogeneous network selection method for disease and microbial association prediction

et al. 2022

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“…Although there are few computational methods for predicting ncRNA−drug resistance, many related association prediction methods are worth discussing. Dayun et al 18 proposed a novel computational framework of MGATMDA to detect microbial−disease associations by multicomponent graph attention networks. First, they generated the latent vectors of nodes from the bipartite graph through the decomposer.…”

Section: ■ Introductionmentioning

confidence: 99%

Graph Neural Network with Self-Supervised Learning for Noncoding RNA–Drug Resistance Association Prediction

Zheng

Qian

et al. 2022

J. Chem. Inf. Model.

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Noncoding RNA(ncRNA) is closely related to drug resistance. Identifying the association between ncRNA and drug resistance is of great significance for drug development. Methods based on biological experiments are often time-consuming and small-scale. Therefore, developing computational methods to distinguish the association between ncRNA and drug resistance is urgent. We develop a computational framework called GSLRDA to predict the association between ncRNA and drug resistance in this work. First, the known ncRNA–drug resistance associations are modeled as a bipartite graph of ncRNA and drug. Then, GSLRDA uses the light graph convolutional network (lightGCN) to learn the vector representation of ncRNA and drug from the ncRNA–drug bipartite graph. In addition, GSLRDA uses different data augmentation methods to generate different views for ncRNA and drug nodes and performs self-supervised learning, further improving the quality of learned ncRNA and drug vector representations through contrastive learning between nodes. Finally, GSLRDA uses the inner product to predict the association between ncRNA and drug resistance. To the best of our knowledge, GSLRDA is the first to apply self-supervised learning in association prediction tasks in the field of bioinformatics. The experimental results show that GSLRDA takes an AUC value of 0.9101, higher than the other eight state-of-the-art models. In addition, case studies including two drugs further illustrate the effectiveness of GSLRDA in predicting the association between ncRNA and drug resistance. The code and data sets of GSLRDA are available at .

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“…NTSHMDA (Luo and Long, 2018) constructs a diseasemicrobe heterogeneity network based on the known similarity between microorganisms and diseases and assigns equal weights to known disease-microbe interactions according to the different contributions of diseases and microorganisms, which is conducive to reducing prediction error. Liu et al (Dayun et al, 2021) established a multi-component graph attention network, which first passed a decomposer to identify nodelevel feature vectors, then combined the feature vectors to obtain a unified embedding vector, which was finally input into a fully connected network to predict microorganisms unknown interactions with the disease. SDLDA (Zeng et al, 2020) extract the linear and nonlinear interactions between lncRNA and diseases through singular value decomposition and neural network and finally unites the linear and nonlinear features into a new feature vector, which is input to the fully connected layer to realize prediction.…”

Section: Introductionmentioning

confidence: 99%

GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions

Xiao

Chen

et al. 2022

Front. Genet.

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The interactions between circular RNAs (circRNAs) and microRNAs (miRNAs) have been shown to alter gene expression and regulate genes on diseases. Since traditional experimental methods are time-consuming and labor-intensive, most circRNA-miRNA interactions remain largely unknown. Developing computational approaches to large-scale explore the interactions between circRNAs and miRNAs can help bridge this gap. In this paper, we proposed a graph convolutional neural network-based approach named GCNCMI to predict the potential interactions between circRNAs and miRNAs. GCNCMI first mines the potential interactions of adjacent nodes in the graph convolutional neural network and then recursively propagates interaction information on the graph convolutional layers. Finally, it unites the embedded representations generated by each layer to make the final prediction. In the five-fold cross-validation, GCNCMI achieved the highest AUC of 0.9312 and the highest AUPR of 0.9412. In addition, the case studies of two miRNAs, hsa-miR-622 and hsa-miR-149-5p, showed that our model has a good effect on predicting circRNA-miRNA interactions. The code and data are available at https://github.com/csuhjhjhj/GCNCMI.

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MGATMDA: Predicting Microbe-Disease Associations via Multi-Component Graph Attention Network

Cited by 20 publications

References 36 publications

A novel bi-directional heterogeneous network selection method for disease and microbial association prediction

A novel bi-directional heterogeneous network selection method for disease and microbial association prediction

Graph Neural Network with Self-Supervised Learning for Noncoding RNA–Drug Resistance Association Prediction

GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions

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