SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

Wang, Shudong; Lin, Boyang; Zhang, Yuanyuan; Qiao, Sibo; Wang, Fuyu; Wu, Wenhao; Ren, Chuanru

doi:10.3390/cells11243984

Cited by 10 publications

(2 citation statements)

References 51 publications

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“…Deep learning is widely used in many research areas of bioinformatics [18], various deep learning-based methods have been applied in DTA prediction, where it can capture complex hidden information from massive data. Öztürk et al [9] proposed DeepDTA, which employs two convolutional neural networks (CNNs) to extract local sequence information and then feed it into several fully connected layers for DTA prediction.…”

Section: Introductionmentioning

confidence: 99%

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug–Target Binding Affinity Prediction

Wang

Song

Zhang

et al. 2023

IJMS

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The accurate prediction of drug–target binding affinity (DTA) is an essential step in drug discovery and drug repositioning. Although deep learning methods have been widely adopted for DTA prediction, the complexity of extracting drug and target protein features hampers the accuracy of these predictions. In this study, we propose a novel model for DTA prediction named MSGNN-DTA, which leverages a fused multi-scale topological feature approach based on graph neural networks (GNNs). To address the challenge of accurately extracting drug and target protein features, we introduce a gated skip-connection mechanism during the feature learning process to fuse multi-scale topological features, resulting in information-rich representations of drugs and proteins. Our approach constructs drug atom graphs, motif graphs, and weighted protein graphs to fully extract topological information and provide a comprehensive understanding of underlying molecular interactions from multiple perspectives. Experimental results on two benchmark datasets demonstrate that MSGNN-DTA outperforms the state-of-the-art models in all evaluation metrics, showcasing the effectiveness of the proposed approach. Moreover, the study conducts a case study based on already FDA-approved drugs in the DrugBank dataset to highlight the potential of the MSGNN-DTA framework in identifying drug candidates for specific targets, which could accelerate the process of virtual screening and drug repositioning.

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Section: Introductionmentioning

confidence: 99%

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug–Target Binding Affinity Prediction

Wang

Song

Zhang

et al. 2023

IJMS

Self Cite

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“…Meanwhile, the greedy layer-wise training of the SAE determines the network parameters layer by layer and accelerates the convergence speed [ 4 ]. By virtue of excellent performance, the SAE has been applied to mechanical fault diagnosis [ 5 , 6 ], disease association prediction [ 7 , 8 ] and network intrusion detection [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks

Lai,

Wang,

Xiang

et al. 2023

Entropy

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The efficiency and cognitive limitations of manual sample labeling result in a large number of unlabeled training samples in practical applications. Making full use of both labeled and unlabeled samples is the key to solving the semi-supervised problem. However, as a supervised algorithm, the stacked autoencoder (SAE) only considers labeled samples and is difficult to apply to semi-supervised problems. Thus, by introducing the pseudo-labeling method into the SAE, a novel pseudo label-based semi-supervised stacked autoencoder (PL-SSAE) is proposed to address the semi-supervised classification tasks. The PL-SSAE first utilizes the unsupervised pre-training on all samples by the autoencoder (AE) to initialize the network parameters. Then, by the iterative fine-tuning of the network parameters based on the labeled samples, the unlabeled samples are identified, and their pseudo labels are generated. Finally, the pseudo-labeled samples are used to construct the regularization term and fine-tune the network parameters to complete the training of the PL-SSAE. Different from the traditional SAE, the PL-SSAE requires all samples in pre-training and the unlabeled samples with pseudo labels in fine-tuning to fully exploit the feature and category information of the unlabeled samples. Empirical evaluations on various benchmark datasets show that the semi-supervised performance of the PL-SSAE is more competitive than that of the SAE, sparse stacked autoencoder (SSAE), semi-supervised stacked autoencoder (Semi-SAE) and semi-supervised stacked autoencoder (Semi-SSAE).

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Identify Disease-Associated MiRNA-miRNA Pairs Through Deep Tensor Factorization and Semi-supervised Learning

Wang,

Pan,

2024

Lecture Notes in Computer Science

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SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

Cited by 10 publications

References 51 publications

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug–Target Binding Affinity Prediction

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug–Target Binding Affinity Prediction

A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks

Identify Disease-Associated MiRNA-miRNA Pairs Through Deep Tensor Factorization and Semi-supervised Learning

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