Predicting Microbe-Disease Association Based on Multiple Similarities and LINE Algorithm

Wang, Yueyue; Lei, Xiujuan; Lu, Cheng; Pan, Yi

doi:10.1109/tcbb.2021.3082183

Cited by 11 publications

(3 citation statements)

References 67 publications

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“…The random walks ceased when the maximum number of iterations for both networks was reached, yielding the final correlation probability matrix. Wang Y. et al (2022) presented the MSLINE model, which constructed a Microbe Disease Heterogeneous Network (MDHN) by integrating known associations and multiple similarities. Subsequently, a random walk algorithm was implemented on the MDHN to learn its structural information.…”

Section: Introductionmentioning

confidence: 99%

MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning

Chen,

Zhang,

et al. 2024

Front. Microbiol.

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IntroductionA growing body of research indicates that microorganisms play a crucial role in human health. Imbalances in microbial communities are closely linked to human diseases, and identifying potential relationships between microbes and diseases can help elucidate the pathogenesis of diseases. However, traditional methods based on biological or clinical experiments are costly, so the use of computational models to predict potential microbe-disease associations is of great importance.MethodsIn this paper, we present a novel computational model called MLFLHMDA, which is based on a Multi-View Latent Feature Learning approach to predict Human potential Microbe-Disease Associations. Specifically, we compute Gaussian interaction profile kernel similarity between diseases and microbes based on the known microbe-disease associations from the Human Microbe-Disease Association Database and perform a preprocessing step on the resulting microbe-disease association matrix, namely, weighting K nearest known neighbors (WKNKN) to reduce the sparsity of the microbe-disease association matrix. To obtain unobserved associations in the microbe and disease views, we extract different latent features based on the geometrical structure of microbes and diseases, and project multi-modal latent features into a common subspace. Next, we introduce graph regularization to preserve the local manifold structure of Gaussian interaction profile kernel similarity and add Lp,q-norms to the projection matrix to ensure the interpretability and sparsity of the model.ResultsThe AUC values for global leave-one-out cross-validation and 5-fold cross validation implemented by MLFLHMDA are 0.9165 and 0.8942+/−0.0041, respectively, which perform better than other existing methods. In addition, case studies of different diseases have demonstrated the superiority of the predictive power of MLFLHMDA. The source code of our model and the data are available on https://github.com/LiangzheZhang/MLFLHMDA_master.

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Section: Introductionmentioning

confidence: 99%

MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning

Chen,

Zhang,

et al. 2024

Front. Microbiol.

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“… Li et al (2020) raised a neural network approach based on the backpropagation of a modified hyperbolic tangent activation function to predict disease-related microbes. Wang et al (2021) applied random walk and graph embedding algorithm LINE to preserve graph structure through first-order and second-order proximity and to learn the latent feature representations of microbes and diseases, afterward obtained new microbe-disease associations by refactoring the representation. Long et al (2021) developed an embedding representation method based on inductive matrix completion and graph attention network to infer the possible associations between microbes and diseases.…”

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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“…A. et al (2017) proposed a method based on known similarities, using a deep traversal method to explore the potential path between microbes and diseases, so as to obtain the potential associations between microbes and diseases. Wang et al (2021) proposed a new computational model, named MSLINE, to infer potential microbe-disease associations by combining multiple similarity and large-scale information network embedding (LINE) based on known associations. Chen et al (2021) construct a Heterogeneous Network for Small Molecule-miRNA Using Bounded Kernel Canonical Regularization to Predict (SM-miRNA) Association Prediction (BNNRSMMA).…”

Section: Introductionmentioning

confidence: 99%

MSIF-LNP: microbial and human health association prediction based on matrix factorization noise reduction for similarity fusion and bidirectional linear neighborhood label propagation

et al. 2023

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Studies have shown that microbes are closely related to human health. Clarifying the relationship between microbes and diseases that cause health problems can provide new solutions for the treatment, diagnosis, and prevention of diseases, and provide strong protection for human health. Currently, more and more similarity fusion methods are available to predict potential microbe-disease associations. However, existing methods have noise problems in the process of similarity fusion. To address this issue, we propose a method called MSIF-LNP that can efficiently and accurately identify potential connections between microbes and diseases, and thus clarify the relationship between microbes and human health. This method is based on matrix factorization denoising similarity fusion (MSIF) and bidirectional linear neighborhood propagation (LNP) techniques. First, we use non-linear iterative fusion to obtain a similarity network for microbes and diseases by fusing the initial microbe and disease similarities, and then reduce noise by using matrix factorization. Next, we use the initial microbe-disease association pairs as label information to perform linear neighborhood label propagation on the denoised similarity network of microbes and diseases. This enables us to obtain a score matrix for predicting microbe-disease relationships. We evaluate the predictive performance of MSIF-LNP and seven other advanced methods through 10-fold cross-validation, and the experimental results show that MSIF-LNP outperformed the other seven methods in terms of AUC. In addition, the analysis of Cystic fibrosis and Obesity cases further demonstrate the predictive ability of this method in practical applications.

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Predicting Microbe-Disease Association Based on Multiple Similarities and LINE Algorithm

Cited by 11 publications

References 67 publications

MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning

MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning

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

MSIF-LNP: microbial and human health association prediction based on matrix factorization noise reduction for similarity fusion and bidirectional linear neighborhood label propagation

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