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This study introduces Homo-RAG, a novel framework that enhances gene function prediction in non-model organisms by integrating homology-guided multi-hop retrieval with evidence-aware ranking. By leveraging biological relationships between zebrafish and human orthologs, the framework effectively retrieves and ranks evidence from diverse databases, achieving a remarkable NDCG@10 score of 0.9879 and a mean reciprocal rank (MRR) of 0.99 across 150 queries. The results demonstrate that Homo-RAG not only retrieves relevant evidence for 99.33% of queries but also emphasizes the importance of evidence quality, with 80% of retrieved documents being query-exclusive.
Evidence weighting in Homo-RAG boosts retrieval effectiveness, achieving near-perfect relevance scores while enhancing the quality of gene function predictions for non-model organisms.
The functional annotation of genes in non-model organisms remains a significant challenge in computational biology, with 20-70% of sequenced genes lacking characterized functions. Traditional homology-based methods are often costly and strongly dependent on high sequence similarity. This study presents Homo-RAG, a framework for large language model-based gene function prediction that integrates homology-guided multi-hop retrieval with evidence-aware ranking. The framework exploits biological relationships between zebrafish and human orthologs to guide evidence acquisition from ZFIN, UniProt, and PubMed through hybrid dense and lexical retrieval. An Evidence Confidence Score (ECS) integrates semantic relevance, entity matching, orthology information, source reliability, and literature association signals to refine the ranking of retrieved evidence. Extensive evaluation across 150 queries and 7,200 retrieved documents shows that evidence weighting parameter of lambda=0.50 improves NDCG@10 to 0.9879 and MRR to 0.99, while retrieving relevant evidence for 99.33% of queries. Furthermore, 80% of the retrieved documents are query-exclusive, indicating that evidence quality complements rather than replaces retrieval relevance. These findings establish Homo-RAG as a practical and robust framework for reliable, evidence-grounded gene function prediction in understudied organisms. The study addresses important limitations of conventional annotation pipelines while identifying opportunities for future improvements in evidence features and attribution mechanisms.
