A graph auto-encoder model for miRNA-disease associations prediction

Li, Zhengwei; Li, Jiashu; Nie, Rencan; You, Zhu‐Hong; Bao, Wenzheng

doi:10.1093/bib/bbaa240

Cited by 84 publications

(38 citation statements)

References 40 publications

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“…In this section, we compared the prediction performance of our model with other state-of-the-art methods, including PBMDA (You et al, 2017 ), GRNMF (Xiao et al, 2018 ), MDHGI (Chen et al, 2018e ), BNPMDA (Chen et al, 2018d ), MCLPMDA (Yu et al, 2019 ), NIMCGCN (Li et al, 2020a ), and GAEMDA (Li et al, 2020b ). We noted that different evaluation metrics and datasets are used in these methods.…”

Section: Resultsmentioning

confidence: 99%

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Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks

Wang

et al. 2021

Front. Genet.

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In recent years, more and more evidence has shown that microRNAs (miRNAs) play an important role in the regulation of post-transcriptional gene expression, and are closely related to human diseases. Many studies have also revealed that miRNAs can be served as promising biomarkers for the potential diagnosis and treatment of human diseases. The interactions between miRNA and human disease have rarely been demonstrated, and the underlying mechanism of miRNA is not clear. Therefore, computational approaches has attracted the attention of researchers, which can not only save time and money, but also improve the efficiency and accuracy of biological experiments. In this work, we proposed a Heterogeneous Graph Attention Networks (GAT) based method for miRNA-disease associations prediction, named HGATMDA. We constructed a heterogeneous graph for miRNAs and diseases, introduced weighted DeepWalk and GAT methods to extract features of miRNAs and diseases from the graph. Moreover, a fully-connected neural networks is used to predict correlation scores between miRNA-disease pairs. Experimental results under five-fold cross validation (five-fold CV) showed that HGATMDA achieved better prediction performance than other state-of-the-art methods. In addition, we performed three case studies on breast neoplasms, lung neoplasms and kidney neoplasms. The results showed that for the three diseases mentioned above, 50 out of top 50 candidates were confirmed by the validation datasets. Therefore, HGATMDA is suitable as an effective tool to identity potential diseases-related miRNAs.

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Section: Resultsmentioning

confidence: 99%

“…Additionally, negative samples were needed for regression training. GAEMDA integrated similarities of diseases and miRNAs as features of nodes, applied a GCN model for further feature extraction, and then used a bilinear decoder for identification (Li et al, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks

Wang

et al. 2021

Front. Genet.

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“…In order to verify the accuracy of our method, the HGCNELMDA method was compared with the following four existing methods, namely FCGCNMDA [32], CNMDA[33], EDTMDA[34] and RKNNMDA [35], for five-fold cross-validation.As shown in Table 1, the AUC of FCGCNMDA, CNMDA, EDTMDA and RKNNMDA were 92.85%, 85.33%, 91.92% and 82.21%, respectively. Among them, the AUC of HGCNELMDA was the highest under five-fold cross-validation, with a value of 93.01%.Therefore, HGCNELMDA was proved to be reliable in miRNA-disease association.…”

Section: Compare With Other Methodsmentioning

confidence: 99%

“…Layer node embedding dimension is the node embedding parameter in GCN hidden layer ℎ , Different parameter values will affect the experimental results. As shown in Figure 5, define ℎ as [32,64,128,256,512], Compared with the AUC results, The validation methods of one-left cross-validation and five-fold cross-validation show that the AUC value presents an upward trend with the increase of node embedding dimension ℎ .The performance of the HGCNELMDA approach is highest when the embedding dimension ℎ is defined as 256.…”

Section: Comparison Of Parameter Sensitivitiesmentioning

confidence: 99%

Computational Method Using Heterogeneous Graph Convolutional Network Model Combined With Reinforcement Layer For MiRNA-Disease Association Prediction

Huang¹,

An²,

Zhang³

et al. 2021

Preprint

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Background: A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challenging task that how to develop more accurate and efficient methods for predicting potential associations between miRNA and disease. Results: In the study, we developed a computational model that combined Heterogeneous Graph Convolutional Network with Enhanced Layer for miRNA-Disease Association prediction (HGCNELMDA). The major improvement of our method lies in through restarting the random walk optimized the original features of nodes and adding a Reinforcement layer to the hidden layer of graph convolutional network retained similar information between nodes in the feature space. In addition, the proposed approach recalculated the influence of neighborhood nodes on target nodes by introducing the attention mechanism. The reliable performance of the HGCNELMDA was certified by the AUC of 93.47% in global leave-one-out cross-validation (LOOCV), and the average AUCs of 93.01% in fivefold cross-validation. Meanwhile, we compared the HGCNELMDA with the state‑of‑the‑art methods. Comparative results indicated that o the HGCNELMDA is very promising and may provide a cost‑effective alternative for miRNA-Disease Association prediction. Moreover, we applied HGCNELMDA to 3 different case studies to predict potential miRNAs related to lung cancer, prostate cancer, and pancreatic cancer. Results showed that 48, 50, and 50 of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, the HGCNELMDA is a reliable method for predicting disease-related miRNAs. Conclusions: The results of the HGCNELMDA method in the LOOCV (leave-one-out cross validation, LOOCV) and 5-cross validations were 93.47% and 93.01%, respectively. Compared with other typical methods, the performance of HGGCNMA is higher. Three cases of lung cancer, prostate cancer, and pancreatic cancer were studied. Among the predicted top 50 candidate miRNAs, 48, 50, and 50 were verified in the biological database HDMMV2.0.Therefore; this further confirms the feasibility and effectiveness of our method. To facilitate extensive studies for future disease-related miRNAs research, we developed a freely available web server called HGCNELMDA is available at http://132.232.17.50:8080/HGCNELMDA.jsp.

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“…DBNMDA ( Chen et al, 2020 ) constructed feature vectors for all miRNA–disease pairs to pretrain restricted Boltzmann machines and put the same amount of positive and negative samples into the deep-belief network to get the final prediction results. Li et al (2021) proposed GAEMDA to identify potential miRNA–disease associations in an end-to-end manner. In multilayer perception machine learning of diverse dimensions of semantic information, the introduction of a graph neural network serves to aggregate the neighborhood information of nodes.…”

Section: Introductionmentioning

confidence: 99%

WVMDA: Predicting miRNA–Disease Association Based on Weighted Voting

et al. 2021

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An increasing number of experiments had verified that miRNA expression is related to human diseases. The miRNA expression profile may be an indicator of clinical diagnosis and provides a new direction for the prevention and treatment of complex diseases. In this work, we present a weighted voting-based model for predicting miRNA–disease association (WVMDA). To reasonably build a network of similarity, we established credibility similarity based on the reliability of known associations and used it to improve the original incomplete similarity. To eliminate noise interference as much as possible while maintaining more reliable similarity information, we developed a filter. More importantly, to ensure the fairness and efficiency of weighted voting, we focus on the design of weighting. Finally, cross-validation experiments and case studies are undertaken to verify the efficacy of the proposed model. The results showed that WVMDA could efficiently identify miRNAs associated with the disease.

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A graph auto-encoder model for miRNA-disease associations prediction

Cited by 84 publications

References 40 publications

Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks

Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks

Computational Method Using Heterogeneous Graph Convolutional Network Model Combined With Reinforcement Layer For MiRNA-Disease Association Prediction

WVMDA: Predicting miRNA–Disease Association Based on Weighted Voting

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