MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes

Hua, Meifang; Yu, Shengsheng; Li, Tianyu; Yang, Xue; Wang, Hong

doi:10.1007/s12539-022-00514-2

Cited by 10 publications

(6 citation statements)

References 57 publications

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“…MVGCNMDA [ 24 ], which analogously adopted the idea of multi-scale and utilized the multi-view graph for data augmentation to predict disease-related microbes.…”

Section: Resultsmentioning

confidence: 99%

“…Long et al [ 23 ] designed a new framework named GATMDA, to represent microbes and diseases and predict associations based on an optimized graph attention network with inductive matrix completion. Furthermore, MVGCNMDA, proposed by Hua et al [ 24 ], utilized the multi-view graph for data augmentation and multi-channel attention to predict disease-related microbes.…”

Section: Introductionmentioning

confidence: 99%

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Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

Zhu,

Hao,

2023

BMC Biol

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Background Enormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early diagnosis and promote precision medicine. Due to the data perturbation and unsatisfactory latent representation, there is a significant room for improvement. Results In this work, we proposed a novel framework, Multi-scale Variational Graph AutoEncoder embedding Wasserstein distance (MVGAEW) to predict disease-related microbes, which had the ability to resist data perturbation and effectively generate latent representations for both microbes and diseases from the perspective of distribution. First, we calculated multiple similarities and integrated them through similarity network confusion. Subsequently, we obtained node latent representations by improved variational graph autoencoder. Ultimately, XGBoost classifier was employed to predict potential disease-related microbes. We also introduced multi-order node embedding reconstruction to enhance the representation capacity. We also performed ablation studies to evaluate the contribution of each section of our model. Moreover, we conducted experiments on common drugs and case studies, including Alzheimer’s disease, Crohn’s disease, and colorectal neoplasms, to validate the effectiveness of our framework. Conclusions Significantly, our model exceeded other currently state-of-the-art methods, exhibiting a great improvement on the HMDAD database.

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“…MVGCNMDA [ 24 ], which analogously adopted the idea of multi-scale and utilized the multi-view graph for data augmentation to predict disease-related microbes.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

Zhu,

Hao,

2023

BMC Biol

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“…Xu et al developed a novel computational method (MDAKRLS) to discover potential MDAs based on the Kronecker regularized least squares in 2021 [29] . Hua et al proposed a multi-view graph convolutional network (MVGCNMDA) to reveal disease-associated microbes using specific data augmentation and multi-view attention blocks in 2022 [30] . Chen et al proposed a method based on heterogeneous network and metapath aggregated graph neural network (MATHNMDA) to predict MDAs in 2022 [31] .…”

Section: Introductionmentioning

confidence: 99%

MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

Liu

Bing

Zhang

et al. 2023

Computational and Structural Biotechnology Journal

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“…Similarly, deep learning has been widely applied to accurate MDA prediction. These methods include deep matrix factorization combining Bayesian personalized ranking (Liu et al, 2020 ), multi-component graph attention network (Liu et al, 2021 ), graph convolutional network (Hua et al, 2022 ), metapath aggregated graph neural network (Chen and Lei, 2022 ), dual network contrastive learning model (Cheng et al, 2022 ), weighted meta-graph-based model (Long and Luo, 2019 ), knowledge graph neural network (Jiang et al, 2022 ), and relation graph convolutional network (Wang Y. et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

SAELGMDA: Identifying human microbe–disease associations based on sparse autoencoder and LightGBM

Wang

Yang

et al. 2023

Front. Microbiol.

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IntroductionIdentification of complex associations between diseases and microbes is important to understand the pathogenesis of diseases and design therapeutic strategies. Biomedical experiment-based Microbe-Disease Association (MDA) detection methods are expensive, time-consuming, and laborious.MethodsHere, we developed a computational method called SAELGMDA for potential MDA prediction. First, microbe similarity and disease similarity are computed by integrating their functional similarity and Gaussian interaction profile kernel similarity. Second, one microbe-disease pair is presented as a feature vector by combining the microbe and disease similarity matrices. Next, the obtained feature vectors are mapped to a low-dimensional space based on a Sparse AutoEncoder. Finally, unknown microbe-disease pairs are classified based on Light Gradient boosting machine.ResultsThe proposed SAELGMDA method was compared with four state-of-the-art MDA methods (MNNMDA, GATMDA, NTSHMDA, and LRLSHMDA) under five-fold cross validations on diseases, microbes, and microbe-disease pairs on the HMDAD and Disbiome databases. The results show that SAELGMDA computed the best accuracy, Matthews correlation coefficient, AUC, and AUPR under the majority of conditions, outperforming the other four MDA prediction models. In particular, SAELGMDA obtained the best AUCs of 0.8358 and 0.9301 under cross validation on diseases, 0.9838 and 0.9293 under cross validation on microbes, and 0.9857 and 0.9358 under cross validation on microbe-disease pairs on the HMDAD and Disbiome databases. Colorectal cancer, inflammatory bowel disease, and lung cancer are diseases that severely threat human health. We used the proposed SAELGMDA method to find possible microbes for the three diseases. The results demonstrate that there are potential associations between Clostridium coccoides and colorectal cancer and one between Sphingomonadaceae and inflammatory bowel disease. In addition, Veillonella may associate with autism. The inferred MDAs need further validation.ConclusionWe anticipate that the proposed SAELGMDA method contributes to the identification of new MDAs.

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MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes

Cited by 10 publications

References 57 publications

Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

SAELGMDA: Identifying human microbe–disease associations based on sparse autoencoder and LightGBM

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