PHPGAT: predicting phage hosts based on multimodal heterogeneous knowledge graph with graph attention network Article Swipe
Fu Liu
,
Zhimiao Zhao
,
Yun Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbaf017
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbaf017
Antibiotic resistance poses a significant threat to global health, making the development of alternative strategies to combat bacterial pathogens increasingly urgent. One such promising approach is the strategic use of bacteriophages (or phages) to specifically target and eradicate antibiotic-resistant bacteria. Phages, being among the most prevalent life forms on Earth, play a critical role in maintaining ecological balance by regulating bacterial communities and driving genetic diversity. Accurate prediction of phage hosts is essential for successfully applying phage therapy. However, existing prediction models may not fully encapsulate the complex dynamics of phage–host interactions in diverse microbial environments, indicating a need for improved accuracy through more sophisticated modeling techniques. In response to this challenge, this study introduces a novel phage–host prediction model, PHPGAT, which leverages a multimodal heterogeneous knowledge graph with the advanced GATv2 (Graph Attention Network v2) framework. The model first constructs a multimodal heterogeneous knowledge graph by integrating phage–phage, host–host, and phage–host interactions to capture the intricate connections between biological entities. GATv2 is then employed to extract deep node features and learn dynamic interdependencies, generating context-aware embeddings. Finally, an inner product decoder is designed to compute the likelihood of interaction between a phage and host pair based on the embedding vectors produced by GATv2. Evaluation results using two datasets demonstrate that PHPGAT achieves precise phage host predictions and outperforms other models. PHPGAT is available at https://github.com/ZhaoZMer/PHPGAT.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbaf017
- OA Status
- gold
- Cited By
- 2
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406604086
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406604086Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bib/bbaf017Digital Object Identifier
- Title
-
PHPGAT: predicting phage hosts based on multimodal heterogeneous knowledge graph with graph attention networkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-22Full publication date if available
- Authors
-
Fu Liu, Zhimiao Zhao, Yun LiuList of authors in order
- Landing page
-
https://doi.org/10.1093/bib/bbaf017Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://doi.org/10.1093/bib/bbaf017Direct OA link when available
- Concepts
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Computer science, Graph, Host (biology), Context (archaeology), Artificial intelligence, Computational biology, Machine learning, Theoretical computer science, Biology, Genetics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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