Multi-similarity fusion-based label propagation for predicting microbes potentially associated with diseases

Yin, Meng-Meng; Gao, Ying-Lian; Shang, Junliang; Zheng, Chun-Hou; Liu, Jin-Xing

doi:10.1016/j.future.2022.04.012

Cited by 3 publications

(2 citation statements)

References 37 publications

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“… Yang et al (2021) designed a novel identification method based on multi-similarities bilinear matrix factorization to find possible microbe-disease associations on a heterogeneous network. Yin et al (2022) used the multiple kernel learning method to fuse similarities of microbe and disease, and then used the label propagation method to make predictions for disease-related potential microbes. Feature learning methods automatically extract features or representations from data through the model, and then reconstruct new microbe-disease associations by the features.…”

Section: Introductionmentioning

confidence: 99%

Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy

Shi

Li²,

Wang³

et al. 2023

Front. Neurosci.

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The interactions between the microbiota and the human host can affect the physiological functions of organs (such as the brain, liver, gut, etc.). Accumulating investigations indicate that the imbalance of microbial community is closely related to the occurrence and development of diseases. Thus, the identification of potential links between microbes and diseases can provide insight into the pathogenesis of diseases. In this study, we propose a deep learning framework (MDAGCAN) based on graph convolutional attention network to identify potential microbe-disease associations. In MDAGCAN, we first construct a heterogeneous network consisting of the known microbe-disease associations and multi-similarity fusion networks of microbes and diseases. Then, the node embeddings considering the neighbor information of the heterogeneous network are learned by applying graph convolutional layers and graph attention layers. Finally, a bilinear decoder using node embedding representations reconstructs the unknown microbe-disease association. Experiments show that our method achieves reliable performance with average AUCs of 0.9778 and 0.9454 ± 0.0038 in the frameworks of Leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, we apply MDAGCAN to predict latent microbes for two high-risk human diseases, i.e., liver cirrhosis and epilepsy, and results illustrate that 16 and 17 out of the top 20 predicted microbes are verified by published literatures, respectively. In conclusion, our method displays effective and reliable prediction performance and can be expected to predict unknown microbe-disease associations facilitating disease diagnosis and prevention.

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Section: Introductionmentioning

confidence: 99%

Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy

Shi

Li²,

Wang³

et al. 2023

Front. Neurosci.

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“…In addition, [ 13 ] proposed a method based on similarity constraint matrix decomposition to predict potential microRNA and disease associations. Several other methods have been proposed to predict microbial and disease associations based on network consistency projection [ 14 ] and multi similarity fusion tag propagation [ 15 ]. However, these methods still have some limitations.…”

Section: Introductionmentioning

confidence: 99%

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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Predicting potential microbe-disease associations based on auto-encoder and graph convolution network

Lu,

Liang,

et al. 2023

BMC Bioinformatics

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The increasing body of research has consistently demonstrated the intricate correlation between the human microbiome and human well-being. Microbes can impact the efficacy and toxicity of drugs through various pathways, as well as influence the occurrence and metastasis of tumors. In clinical practice, it is crucial to elucidate the association between microbes and diseases. Although traditional biological experiments accurately identify this association, they are time-consuming, expensive, and susceptible to experimental conditions. Consequently, conducting extensive biological experiments to screen potential microbe-disease associations becomes challenging. The computational methods can solve the above problems well, but the previous computational methods still have the problems of low utilization of node features and the prediction accuracy needs to be improved. To address this issue, we propose the DAEGCNDF model predicting potential associations between microbes and diseases. Our model calculates four similar features for each microbe and disease. These features are fused to obtain a comprehensive feature matrix representing microbes and diseases. Our model first uses the graph convolutional network module to extract low-rank features with graph information of microbes and diseases, and then uses a deep sparse Auto-Encoder to extract high-rank features of microbe-disease pairs, after which the low-rank and high-rank features are spliced to improve the utilization of node features. Finally, Deep Forest was used for microbe-disease potential relationship prediction. The experimental results show that combining low-rank and high-rank features helps to improve the model performance and Deep Forest has better classification performance than the baseline model.

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Multi-similarity fusion-based label propagation for predicting microbes potentially associated with diseases

Cited by 3 publications

References 37 publications

Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy

Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy

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

Predicting potential microbe-disease associations based on auto-encoder and graph convolution network

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