Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs Article Swipe
Zhecheng Zhou
,
Linlin Zhuo
,
Xiangzheng Fu
,
Quan Zou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbad483
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbad483
Exploring microbial stress responses to drugs is crucial for the advancement of new therapeutic methods. While current artificial intelligence methodologies have expedited our understanding of potential microbial responses to drugs, the models are constrained by the imprecise representation of microbes and drugs. To this end, we combine deep autoencoder and subgraph augmentation technology for the first time to propose a model called JDASA-MRD, which can identify the potential indistinguishable responses of microbes to drugs. In the JDASA-MRD model, we begin by feeding the established similarity matrices of microbe and drug into the deep autoencoder, enabling to extract robust initial features of both microbes and drugs. Subsequently, we employ the MinHash and HyperLogLog algorithms to account intersections and cardinality data between microbe and drug subgraphs, thus deeply extracting the multi-hop neighborhood information of nodes. Finally, by integrating the initial node features with subgraph topological information, we leverage graph neural network technology to predict the microbes’ responses to drugs, offering a more effective solution to the ’over-smoothing’ challenge. Comparative analyses on multiple public datasets confirm that the JDASA-MRD model’s performance surpasses that of current state-of-the-art models. This research aims to offer a more profound insight into the adaptability of microbes to drugs and to furnish pivotal guidance for drug treatment strategies. Our data and code are publicly available at: https://github.com/ZZCrazy00/JDASA-MRD.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbad483
- https://academic.oup.com/bib/article-pdf/25/1/bbad483/54943720/bbad483.pdf
- OA Status
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- Cited By
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- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4390563851Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bib/bbad483Digital Object Identifier
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Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-22Full publication date if available
- Authors
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Zhecheng Zhou, Linlin Zhuo, Xiangzheng Fu, Quan ZouList of authors in order
- Landing page
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https://doi.org/10.1093/bib/bbad483Publisher landing page
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https://academic.oup.com/bib/article-pdf/25/1/bbad483/54943720/bbad483.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://academic.oup.com/bib/article-pdf/25/1/bbad483/54943720/bbad483.pdfDirect OA link when available
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Autoencoder, Computer science, Leverage (statistics), Artificial intelligence, Machine learning, Deep learningTop concepts (fields/topics) attached by OpenAlex
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38Total citation count in OpenAlex
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2025: 17, 2024: 21Per-year citation counts (last 5 years)
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59Number of works referenced by this work
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