BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencoders Article Swipe
Xianzhi Liu
,
Mingmin Liang
,
Yu Ge
,
Shichang Tang
,
Ona Wu
,
Bin Zeng
,
Lei Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fgene.2025.1618472
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fgene.2025.1618472
Introduction Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ecological health, and drug development. Methods In this manuscript, we introduce an innovative computational model named BANSMDA, which integrates Bilinear Attention Networks with sparse autoencoder to uncover hidden connections between microbes and diseases. In BANSMDA, we first constructed a heterogeneous microbe-disease network by integrating multiple Gaussian similarity measures for diseases and microbes, along with known microbe-disease associations. And then, we employed a BAN-based autoencoder and a sparse autoencoder module to learn node representations within this newly constructed heterogeneous network. Finally, we evaluated the prediction performance of BANSMDA using a 5-fold cross-validation framework. Conclusion Experiments results showed that BANSMDA achieved superior performance compared to other cutting-edge methods. To further assess its effectiveness, we carried out case studies on two common diseases (including Asthma and Colorectal carcinoma) and two important microbial genera (including Escherichia and Bacteroides ), and in the top 20 predicted microbes, there were 19 and 20 having been confirmed by published literature respectively. Besides, in the top 20 predicted diseases, there were 19 and 19 having been confirmed by published literature separately. Therefore, it is easy to conclude that BANSMDA can achieve satisfactory prediction ability.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fgene.2025.1618472
- OA Status
- gold
- Cited By
- 1
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413243583
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413243583Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fgene.2025.1618472Digital Object Identifier
- Title
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BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencodersWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-08Full publication date if available
- Authors
-
Xianzhi Liu, Mingmin Liang, Yu Ge, Shichang Tang, Ona Wu, Bin Zeng, Lei WangList of authors in order
- Landing page
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https://doi.org/10.3389/fgene.2025.1618472Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fgene.2025.1618472Direct OA link when available
- Concepts
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Autoencoder, Computer science, Artificial intelligence, Node (physics), Machine learning, Similarity (geometry), Disease, Bilinear interpolation, Deep learning, Medicine, Structural engineering, Engineering, Computer vision, Image (mathematics), PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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