Kavli Affiliate: Cheng Peng
| First 5 Authors: Jiayu Shang, Jiayu Shang, , ,
| Summary:
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.
| Search Query: ArXiv Query: search_query=au:”Cheng Peng”&id_list=&start=0&max_results=3