From genomic signals to prediction tools: a critical feature analysis and rigorous benchmark for phage–host prediction

作者：Jiayu Shang, Cheng Peng, Jiaojiao Guan, Dehan Cai, Donglin Wang, Yanni Sun · 发表于：Briefings in Bioinformatics · 年份：2025 · DOI：10.1093/bib/bbaf626 · 被引用次数：9 · 研究领域：Bacteriophages and microbial interactions、Machine Learning in Bioinformatics、vaccines and immunoinformatics approaches

Accurate prediction of virus-host interactions is critical for understanding viral ecology and developing applications like phage therapy. However, the growing number of computational tools has created a complex landscape, making direct performance comparison challenging due to inconsistent benchmarks and varying usability. Here, we provide a systematic review and a rigorous benchmark of 27 virus-host prediction tools. We formulate the host prediction task into two primary frameworks-link prediction and multi-class classification-and construct two benchmark datasets to evaluate tool performance in distinct scenarios: a database-centric dataset (RefSeq-VHDB) and a metagenomic discovery dataset (MetaHiC-VHDB). Our results reveal that no single tool is universally optimal. Performance is highly context-dependent, with tools like CHERRY and iPHoP demonstrating robust, broad applicability, while others, such as RaFAH and PHIST, excel in specific contexts. We further identify a critical trade-off between predictive accuracy, prediction rate, and computational cost. This work serves as a practical guide for researchers and establishes a standardized benchmark to drive future innovation in deciphering complex virus-host interactions.