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Drug Repositioning Based on Deep Sparse Autoencoder and Drug–Disease Similarity

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Abstract

Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug–disease heterogeneous networks to extract drug–disease features. However, these methods faced difficulty when we are using structurally simple models to deal with complex heterogeneous networks. Therefore, in this study, the researchers introduced a drug repositioning method named DRDSA. The method utilizes a deep sparse autoencoder and integrates drug–disease similarities. First, the researchers constructed a drug–disease feature network by incorporating information from drug chemical structure, disease semantic data, and existing known drug–disease associations. Then, we learned the low-dimensional representation of the feature network using a deep sparse autoencoder. Finally, we utilized a deep neural network to make predictions on new drug–disease associations based on the feature representation. The experimental results show that our proposed method has achieved optimal results on all four benchmark datasets, especially on the CTD dataset where AUC and AUPR reached 0.9619 and 0.9676, respectively, outperforming other baseline methods. In the case study, the researchers predicted the top ten antiviral drugs for COVID-19. Remarkably, six out of these predictions were subsequently validated by other literature sources.

Graphical Abstract

Schematic diagrams of data processing and DRDSA model. A Construction of drug and disease feature vectors, B The workflow of DRDSA model.

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Data availability

Publicly available datasets were analyzed in this study. These data can be found here: http://ctdbase.org/; https://github.com/luckymengmeng/HDVD.

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Funding

This work was supported by the National Natural Science Foundation of China (62272288, 61972451), the Shenzhen Science and Technology Program (KQTD20200820113106007), the Fundamental Research Funds for the Central Universities, Shaanxi Normal University (GK202302006), and the Hunan Provincial Natural Science Foundation of China (2023JJ30411).

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Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Song Lei & Xiujuan Lei

  2. College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Ming Chen

  3. Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Yi Pan

  4. Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China

    Yi Pan

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  1. Song Lei
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Correspondence to Xiujuan Lei or Yi Pan.

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Lei, S., Lei, X., Chen, M. et al. Drug Repositioning Based on Deep Sparse Autoencoder and Drug–Disease Similarity. Interdiscip Sci Comput Life Sci 16, 160–175 (2024). https://doi.org/10.1007/s12539-023-00593-9

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