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Interactome-Seq: A Protocol for Domainome Library Construction, Validation and Selection by Phage Display and Next Generation Sequencing

Interactome-Seq: A Protocol for Domainome Library Construction, Validation and Selection by Phage Display and Next Generation Sequencing

Published on: October 3, 2018

A deep Siamese network framework for precision phage selection in pulmonary infections.

Xinghong Wang¹, Mingpeng Fu¹, Shigang Lin²

¹Department of Clinical Laboratory, The Second Affiliated Hospital of Hainan Medical University, Haikou, China.

Frontiers in Medicine

|February 20, 2026

Summary

This study introduces a deep Siamese network to precisely select bacteriophages for treating drug-resistant pulmonary infections. The AI framework improves phage selection accuracy for effective phage therapy.

Keywords:

Siamese network artificial intelligence phage selection phage therapy pulmonary infection

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Last Updated: Feb 21, 2026

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A Precise Pathogen Delivery and Recovery System for Murine Models of Secondary Bacterial Pneumonia

A Precise Pathogen Delivery and Recovery System for Murine Models of Secondary Bacterial Pneumonia

Published on: September 21, 2019

Area of Science:

Biotechnology
Infectious Diseases
Computational Biology

Background:

Pulmonary infections are a major global health concern, especially in patients with chronic lung diseases.
Antimicrobial resistance complicates treatment, necessitating novel therapeutic strategies like phage therapy.
Current artificial intelligence (AI) methods for phage selection lack precision in predicting phage-bacterial interactions.

Purpose of the Study:

To develop a deep Siamese network for precise phage selection in pulmonary infections.
To enhance the accuracy of predicting phage-bacterial host interactions for clinical translation of phage therapy.

Main Methods:

Genomic sequences of phages and hosts were encoded using k-mer segmentation and the skip-gram model.
Convolutional Neural Networks (CNNs) and Transformers were used to extract local and global genomic features.
A deep Siamese network framework fused features to predict phage-host interactions.

Main Results:

The proposed deep Siamese network demonstrated superior performance in identifying specific phages for bacterial hosts.
The method achieved high accuracy in predicting phage-bacterial interactions, overcoming limitations of existing AI approaches.

Conclusions:

The developed AI framework shows significant potential for precision phage selection in treating pulmonary infections.
This approach supports the clinical application of phage therapy against drug-resistant bacterial pathogens.