Enhancing Strain-level Phage-Host Prediction through Experimentally Vali-dated Negatives and Feature Optimization Strategies Article Swipe
Min Li
,
G Y Liu
,
Wenchen Song
,
Jianqiang Li
,
Lijia Ma
,
Minfeng Xiao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.31.656987
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.05.31.656987
Accurate prediction of phage-host interactions at the strain level is critical for understanding microbial ecology and for developing phage-based therapeutics. However, existing models are limited by the lack of experimentally validated negative interactions and inconsistencies in data construction strategies. In this study, we present a large-scale phage-host interaction dataset com-prising 13,000 experimentally verified links between 125 Klebsiella pneumoniae((K. pneumoniae) phages and 104 K. pneumoniae strains. Using this unique resource, we systematically evaluate the impact of negative data construction meth-ods, feature extraction strategies, and machine learning algorithms on predictive performance. We show that randomly generated negatives significantly inflate model accuracy, while models trained on experimental negatives yield more realistic and robust results. Furthermore, protein-derived features outperform DNA-based features across various data conditions. Notably, models using only tail protein sequences achieve performance comparable to those using full-genome sequences, offering a time-efficient alternative without compromising accuracy. Finally, interpretable machine learning reveals amino acid preferences in both phages and hosts that align with known infection mechanisms and suggest novel determinants such as antitranscriptional proteins. Our findings highlight best practices for constructing high-fidelity strain-level phage-host prediction models. The dataset and insights presented here provide a valuable benchmark for future studies and lay the foundation for more biologically grounded, interpretable modeling frameworks in viromics and micro-biome research.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.05.31.656987
- https://www.biorxiv.org/content/biorxiv/early/2025/06/03/2025.05.31.656987.full.pdf
- OA Status
- green
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411035396
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411035396Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.05.31.656987Digital Object Identifier
- Title
-
Enhancing Strain-level Phage-Host Prediction through Experimentally Vali-dated Negatives and Feature Optimization StrategiesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-03Full publication date if available
- Authors
-
Min Li, G Y Liu, Wenchen Song, Jianqiang Li, Lijia Ma, Minfeng XiaoList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.05.31.656987Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/06/03/2025.05.31.656987.full.pdfDirect link to full text PDF
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-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2025/06/03/2025.05.31.656987.full.pdfDirect OA link when available
- Concepts
-
Feature (linguistics), Strain (injury), Host (biology), Computer science, Biology, Computational biology, Genetics, Philosophy, Anatomy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.resource, | 68 |
| abstract_inverted_index.sequences | 127 |
| abstract_inverted_index.validated | 30 |
| abstract_inverted_index.Klebsiella | 56 |
| abstract_inverted_index.algorithms | 85 |
| abstract_inverted_index.comparable | 130 |
| abstract_inverted_index.developing | 17 |
| abstract_inverted_index.extraction | 80 |
| abstract_inverted_index.foundation | 198 |
| abstract_inverted_index.frameworks | 205 |
| abstract_inverted_index.mechanisms | 161 |
| abstract_inverted_index.outperform | 114 |
| abstract_inverted_index.phage-host | 3, 46, 179 |
| abstract_inverted_index.pneumoniae | 63 |
| abstract_inverted_index.prediction | 1, 180 |
| abstract_inverted_index.predictive | 87 |
| abstract_inverted_index.sequences, | 135 |
| abstract_inverted_index.alternative | 139 |
| abstract_inverted_index.com-prising | 49 |
| abstract_inverted_index.conditions. | 120 |
| abstract_inverted_index.full-genome | 134 |
| abstract_inverted_index.interaction | 47 |
| abstract_inverted_index.large-scale | 45 |
| abstract_inverted_index.micro-biome | 209 |
| abstract_inverted_index.performance | 129 |
| abstract_inverted_index.phage-based | 18 |
| abstract_inverted_index.pneumoniae) | 58 |
| abstract_inverted_index.preferences | 150 |
| abstract_inverted_index.strategies, | 81 |
| abstract_inverted_index.strategies. | 38 |
| abstract_inverted_index.Furthermore, | 111 |
| abstract_inverted_index.biologically | 201 |
| abstract_inverted_index.compromising | 141 |
| abstract_inverted_index.constructing | 176 |
| abstract_inverted_index.construction | 37, 77 |
| abstract_inverted_index.determinants | 165 |
| abstract_inverted_index.experimental | 103 |
| abstract_inverted_index.interactions | 4, 32 |
| abstract_inverted_index.performance. | 88 |
| abstract_inverted_index.strain-level | 178 |
| abstract_inverted_index.high-fidelity | 177 |
| abstract_inverted_index.interpretable | 144, 203 |
| abstract_inverted_index.significantly | 95 |
| abstract_inverted_index.therapeutics. | 19 |
| abstract_inverted_index.understanding | 12 |
| abstract_inverted_index.experimentally | 29, 51 |
| abstract_inverted_index.pneumoniae((K. | 57 |
| abstract_inverted_index.systematically | 70 |
| abstract_inverted_index.time-efficient | 138 |
| abstract_inverted_index.inconsistencies | 34 |
| abstract_inverted_index.protein-derived | 112 |
| abstract_inverted_index.antitranscriptional | 168 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.2188448 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |