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Immune surveillance is an integral part of the innate immune system, involving the continuous monitoring of peripheral tissues to detect and respond to pathogens, infected cells, or cancerous cells. This surveillance is conducted primarily by natural killer (NK) cells and phagocytes, which employ distinct but complementary mechanisms to identify and eliminate threats.
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Updated: Oct 19, 2025

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
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DeepHost: phage host prediction with convolutional neural network.

Wang Ruohan1, Zhang Xianglilan2, Wang Jianping1

  • 1Department of Computer Science at City University of Hong Kong.

Briefings in Bioinformatics
|September 23, 2021
PubMed
Summary
This summary is machine-generated.

DeepHost accurately predicts phage hosts using a novel genome encoding method and convolutional neural networks. This tool aids in characterizing novel phage genomes discovered through next-generation sequencing.

Keywords:
convolutional neural networkgenome encodingphage–host relationship

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing rapidly increases the number of known phage genomes.
  • Identifying phage hosts is crucial but challenging due to high genomic diversity and limitations of culture-based methods.

Purpose of the Study:

  • To develop an accurate and efficient computational tool for predicting phage hosts from genomic data.
  • To address the challenge of uncharacterized phage hosts from sequencing data.

Main Methods:

  • Developed DeepHost, a phage host prediction tool utilizing a novel genome encoding strategy with spaced k-mer pairs to tolerate sequence variations.
  • Employed a convolutional neural network for predicting phage host taxonomies.

Main Results:

  • DeepHost achieved high prediction accuracies: 96.05% at the genus level and 90.78% at the species level.
  • Outperformed existing tools by 10.16-30.48% and showed comparable performance to BLAST.
  • Demonstrated utility for genomes with low homology to existing datasets, achieving 38.00% genus-level accuracy.

Conclusions:

  • DeepHost provides an alignment-free, faster alternative to BLAST for phage host prediction, especially for large datasets.
  • The tool effectively characterizes novel phage genomes, advancing phage biology research.