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Predicting Microbe-Disease Associations Based on a Linear Neighborhood Label Propagation Method with Multi-order Similarity Fusion Learning

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Abstract

Computational approaches employed for predicting potential microbe-disease associations often rely on similarity information between microbes and diseases. Therefore, it is important to obtain reliable similarity information by integrating multiple types of similarity information. However, existing similarity fusion methods do not consider multi-order fusion of similarity networks. To address this problem, a novel method of linear neighborhood label propagation with multi-order similarity fusion learning (MOSFL-LNP) is proposed to predict potential microbe-disease associations. Multi-order fusion learning comprises two parts: low-order global learning and high-order feature learning. Low-order global learning is used to obtain common latent features from multiple similarity sources. High-order feature learning relies on the interactions between neighboring nodes to identify high-order similarities and learn deeper interactive network structures. Coefficients are assigned to different high-order feature learning modules to balance the similarities learned from different orders and enhance the robustness of the fusion network. Overall, by combining low-order global learning with high-order feature learning, multi-order fusion learning can capture both the shared and unique features of different similarity networks, leading to more accurate predictions of microbe-disease associations. In comparison to six other advanced methods, MOSFL-LNP exhibits superior prediction performance in the leave-one-out cross-validation and 5-fold validation frameworks. In the case study, the predicted 10 microbes associated with asthma and type 1 diabetes have an accuracy rate of up to 90% and 100%, respectively.

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Data availability

All information of these data was collected from the public database HMDAD (http://www.cuilab.cn/hmdad) and Disbiome database (https://disbiome.ugent.be/home). The data and source code can be freely downloaded from: https://github.com/RuiBingo/MOSFL-LNP.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (62002070, 82001331) and the Science and Technology Plan Project of Guangzhou City (202102021236).

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Authors and Affiliations

  1. School of Computer, Guangdong University of Technology, Guangzhou, 510000, China

    Ruibin Chen, Guobo Xie, Zhiyi Lin, Guosheng Gu, Yi Yu & Junrui Yu

  2. Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China

    Zhenguo Liu

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Contributions

Conceptualization: [Rui-Bin Chen, Guo-Bo Xie]; methodology: [Rui-Bin Chen, Guo-Bo Xie]; formal analysis and investigation: [Rui-Bin Chen, Zhi- Yi Lin, Guo-Sheng Gu]; validation: [Rui-Bin Chen]; writing - original draft preparation: [Rui-Bin Chen, Zhi-Yi Lin, Guo-Sheng Gu]; writing -review & editing: [Rui-Bin Chen, Guo-Bo Xie, Zhi-Yi Lin, Guo-Sheng Gu, Yi Yu, Jun-Rui Yu, Zhen-Guo Liu]; funding acquisition: [Guo-Bo Xie, Zhi-Yi Lin, Zhen-Guo Liu]; resources: [Yi Yu, Jun-Rui Yu, Zhen-Guo Liu].

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Correspondence to Zhiyi Lin, Guosheng Gu or Zhenguo Liu.

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Chen, R., Xie, G., Lin, Z. et al. Predicting Microbe-Disease Associations Based on a Linear Neighborhood Label Propagation Method with Multi-order Similarity Fusion Learning. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00607-0

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