A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

Shi, Zhuangwei; Zhang, Han; Chen, Jin; Quan, Xiongwen; Yin, Yanbin

doi:10.1186/s12859-021-04073-z

Cited by 61 publications

(34 citation statements)

References 49 publications

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“…The SIMCCDA [ 39 ] is developed by Li et al to predict the associations between circRNAs and diseases, which uses the PCA algorithm for feature extraction and dimensionality reduction, after which the Speedup Inductive Matrix Completion (SIMC) algorithm is used by it to perform the calculation of the prediction score matrix. The VGAELDA [ 40 ] integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. The GATMDA [ 41 ] using graph attention networks with inductive matrix completion for human microbe-disease associations prediction.…”

Section: Resultsmentioning

confidence: 99%

CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Kuang

Deng

2021

BMC Bioinformatics

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Background The existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of diseases. However, the relationships between the vast majority of circRNAs and diseases are still unclear, and more experiments are needed to study the mechanism of circRNAs. Nowadays, some scholars use the attributes between circRNAs and diseases to study and predict their associations. Nonetheless, most of the existing experimental methods use less information about the attributes of circRNAs, which has a certain impact on the accuracy of the final prediction results. On the other hand, some scholars also apply experimental methods to predict the associations between circRNAs and diseases. But such methods are usually expensive and time-consuming. Based on the above shortcomings, follow-up research is needed to propose a more efficient calculation-based method to predict the associations between circRNAs and diseases. Results In this study, a novel algorithm (method) is proposed, which is based on the Graph Convolutional Network (GCN) constructed with Random Walk with Restart (RWR) and Principal Component Analysis (PCA) to predict the associations between circRNAs and diseases (CRPGCN). In the construction of CRPGCN, the RWR algorithm is used to improve the similarity associations of the computed nodes with their neighbours. After that, the PCA method is used to dimensionality reduction and extract features, it makes the connection between circRNAs with higher similarity and diseases closer. Finally, The GCN algorithm is used to learn the features between circRNAs and diseases and calculate the final similarity scores, and the learning datas are constructed from the adjacency matrix, similarity matrix and feature matrix as a heterogeneous adjacency matrix and a heterogeneous feature matrix. Conclusions After 2-fold cross-validation, 5-fold cross-validation and 10-fold cross-validation, the area under the ROC curve of the CRPGCN is 0.9490, 0.9720 and 0.9722, respectively. The CRPGCN method has a valuable effect in predict the associations between circRNAs and diseases.

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Section: Resultsmentioning

confidence: 99%

CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Kuang

Deng

2021

BMC Bioinformatics

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“…Hence, a graph autoencoder with Y as input and F as output can obtain the optimal solution of Equation ( 12 ). Simulating the label propagation algorithm through the reconstruction procedure of graph autoencoder, has been validated as an efficient way for biological association prediction in previous research [ 41 , 42 ].…”

Section: Methodsmentioning

confidence: 99%

“…Graph Neural Networks (GNN) [ 39 ] have been proposed in deep learning on graphs. Thus, there are some recent studies for predicting associations among biological entities based on GNNs [ 40 , 41 , 42 ]. Li et al [ 43 ] implemented an inductive matrix completion algorithm based on Graph Convolutional Networks (GCN) for predicting miRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism

et al. 2022

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Many studies have clarified that microRNAs (miRNAs) are associated with many human diseases. Therefore, it is essential to predict potential miRNA-disease associations for disease pathogenesis and treatment. Numerous machine learning and deep learning approaches have been adopted to this problem. In this paper, we propose a Neural Inductive Matrix completion-based method with Graph Autoencoders (GAE) and Self-Attention mechanism for miRNA-disease associations prediction (NIMGSA). Some of the previous works based on matrix completion ignore the importance of label propagation procedure for inferring miRNA-disease associations, while others cannot integrate matrix completion and label propagation effectively. Varying from previous studies, NIMGSA unifies inductive matrix completion and label propagation via neural network architecture, through the collaborative training of two graph autoencoders. This neural inductive matrix completion-based method is also an implementation of self-attention mechanism for miRNA-disease associations prediction. This end-to-end framework can strengthen the robustness and preciseness of both matrix completion and label propagation. Cross validations indicate that NIMGSA outperforms current miRNA-disease prediction methods. Case studies demonstrate that NIMGSA is competent in detecting potential miRNA-disease associations.

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“…To further illustrate the advantages of the proposed model, several existing methods based on embedding are compared with GBDTLRL2D, such as LDAH2V, VGAELDA (Shi et al, 2021), and GCNMDA (Long et al, 2020). The 10-fold crossvalidation is selected to measure the performance.…”

Section: Performance Comparison With Existing Methodsmentioning

confidence: 99%

“…A model proposed by Zhou et al (2021) uses high-order proximity reserved embedding to embed nodes into the network. The model VGAELDA, which integrates variational reasoning and graph autoencoder, is proposed by Shi et al (2021). A multi-label fusion collaborative matrix decomposition (MLFCMF) method is proposed by Gao et al (2021) to predict lncRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

Duan

Kuang

Wang

et al. 2021

Front. Cell Dev. Biol.

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In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA–disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA–disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA–disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.

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A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

Cited by 61 publications

References 49 publications

CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism

GBDTLRL2D Predicts LncRNA–Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network

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