{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:56:18Z","timestamp":1781160978061,"version":"3.54.1"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T00:00:00Z","timestamp":1780963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Guangxi province of Guodong Li","award":["2026GXNSFAA00640099"],"award-info":[{"award-number":["2026GXNSFAA00640099"]}]},{"name":"Guangxi Center for Applied Mathematics","award":["GuikeAD25069086"],"award-info":[{"award-number":["GuikeAD25069086"]}]},{"name":"Guangxi Academy of Artificial Intelligence"},{"name":"Innovation Project of GUET Graduate Education","award":["2026YCXS142"],"award-info":[{"award-number":["2026YCXS142"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Viruses"],"abstract":"<jats:p>Accurate viral protein function annotation is essential for genomic surveillance, yet conventional retrieval-augmented generation (RAG) pipelines often fragment biological evidence into fixed-length text chunks, disrupting relationships among ORFs, annotations, structural domains, sequence motifs, residue mappings, and model-derived attention evidence. We propose ViroBioTree, a tree-structured biological evidence retrieval framework for downstream viral protein evidence review rather than a new primary annotation classifier. Built as an evidence organization layer on ViralMultiNet-derived ORF-level predictions and annotations, ViroBioTree converts sequence, annotation, structure, and attention evidence into typed biological nodes and traceable edges, then performs deterministic multi-channel recall, evidence-aware reranking, balanced TopK selection, rule-based verification, and node-cited report generation. In a demo benchmark, ViroBioTree achieved its strongest deterministic proxy performance on structure-explanation tasks, with Precision@K = 1.0, Recall@K = 1.0, and diversity = 0.52; these values reflect expected node-type and tag agreement rather than independent biological correctness. A bounded full-scale SARS-CoV-2 index contained 39,800 ORF rows, 80,000 attention records, 199,418 nodes, and 495,886 edges. In a stratified full20k diagnostic evaluation, ViroBioTree showed task-dependent advantages over LlamaIndex vector retrieval for conflict detection, evidence retrieval, and structure explanation, while LlamaIndex remained competitive or stronger for annotation-rich function annotation. A cross-family Influenza A Virus (IAV) diagnostic audit showed that the schema can represent IAV evidence namespaces while explicitly exposing missing formal ORF inputs, missing attention evidence, and unavailable residue\/PDB assertions. Supplementary robustness, external sanity-check, diversity-risk, expert-evaluation, domain-tool positioning, and cross-family audit analyses supported traceability, report quality, and conservative evidence handling, but also showed that stable Precision@K under query perturbation does not necessarily imply stable retrieved evidence sets. ViroBioTree operates offline and deterministically, but does not address raw-read assembly, base calling, primary ORF prediction, or wet-lab validation. Its results should be interpreted as proxy and expert-reviewed evidence for traceable viral protein evidence retrieval and report generation rather than as direct validation of biological function annotation.<\/jats:p>","DOI":"10.3390\/v18060656","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T05:50:58Z","timestamp":1781157058000},"page":"656","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ViroBioTree: A Tree-Structured Biological Evidence Retrieval Framework for Viral Protein Function Annotation"],"prefix":"10.3390","volume":"18","author":[{"given":"Tinglian","family":"Lai","sequence":"first","affiliation":[{"name":"School of Mathematics and Computational Science, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuguo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Data Science, Changji University, Changji 831100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1275-2982","authenticated-orcid":false,"given":"Guodong","family":"Li","sequence":"additional","affiliation":[{"name":"Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541002, China"},{"name":"Center for Applied Mathematics of Guangxi, Guilin University of Electronic Technology, Guilin 541002, China"},{"name":"Academy of Excellent Engineers, Guangxi Artificial Intelligence College, Nanning 530201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyan","family":"Hua","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computational Science, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145124","DOI":"10.1016\/j.scitotenv.2021.145124","article-title":"Early warning of COVID-19 via wastewater-based epidemiology: Potential and bottlenecks","volume":"767","author":"Zhu","year":"2021","journal-title":"Sci. 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