Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection

Fan, Yongxian; Chen, Meijun; Zhu, Qingqi; Wang, Wanru

doi:10.3389/fbioe.2020.00831

Cited by 15 publications

(5 citation statements)

References 49 publications

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“…To validate the reliability of the MSIF-LNP model, we compare MSIF-LNP with NTSHMDA ( Luo and Long, 2018 ), KATZHMDA ( Chen et al, 2017 ), NBLPIHMDA ( Wang et al, 2019 ), BiRWMP ( Shen et al, 2018 ) BPNNHMDA ( Li et al, 2020 ), HMDA-Pred ( Fan et al, 2020 ) and LRLSHMDA ( Wang et al, 2017 ) were compared with seven prediction methods. The comparison results under 10-flod-CV are shown in Figure 5 .…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…NTSHMDA [31] is a model based on random walk with restart for microbe-disease associations prediction. HMDA-Pred [32] integrated multiple data types and adopted the Network Consistency Projection (NCP) technique to detect latent microbe-disease associations. BPNNHMDA [33] designed a novel neural network to infer microbe-disease associations.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder

et al. 2022

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Background Clinical studies show that microorganisms are closely related to human health, and the discovery of potential associations between microbes and drugs will facilitate drug research and development. However, at present, few computational methods for predicting microbe–drug associations have been proposed. Results In this work, we proposed a novel computational model named GSAMDA based on the graph attention network and sparse autoencoder to infer latent microbe–drug associations. In GSAMDA, we first built a heterogeneous network through integrating known microbe–drug associations, microbe similarities and drug similarities. And then, we adopted a GAT-based autoencoder and a sparse autoencoder module respectively to learn topological representations and attribute representations for nodes in the newly constructed heterogeneous network. Finally, based on these two kinds of node representations, we constructed two kinds of feature matrices for microbes and drugs separately, and then, utilized them to calculate possible association scores for microbe–drug pairs. Conclusion A novel computational model is proposed for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder. Compared with other five state-of-the-art competitive methods, the experimental results illustrated that our model can achieve better performance. Moreover, case studies on two categories of representative drugs and microbes further demonstrated the effectiveness of our model as well.

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“…Then, an encoder consisting of a conditional random field and an attention mechanism and a decoder layer are constructed to learn the effective embedding of nodes and score the lncRNA-disease association. Experimental results show that because the GCRFLDA model uses the LNF ( Fan et al., 2020 ) method to fuse similarity information as edge information of nodes and incorporates the attention mechanism, the model has good potential relevance prediction and strong robustness. The model achieved high AUC in four benchmark datasets.…”

Section: Machine Learning-based Modelsmentioning

confidence: 99%

Recent advances in machine learning methods for predicting LncRNA and disease associations

Tan

Li²,

Zhang³

et al. 2022

Front. Cell. Infect. Microbiol.

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Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists’ understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.

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Inferring Disease-Associated Microbes Based on Multi-Data Integration and Network Consistency Projection

Cited by 15 publications

References 49 publications

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

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

GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder

Recent advances in machine learning methods for predicting LncRNA and disease associations

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