Homo-RAG：基于同源性引导检索增强生成的跨物种基因功能预测
Homo-RAG: Homology-Guided Retrieval-Augmented Generation for Cross-Species Gene Function Prediction
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中文总结 AI 辅助
本研究提出Homo-RAG框架，整合同源性引导多跳检索与证据感知排序，实现跨物种基因功能预测，在150个查询及7200份文档的评估中表现优异，为未充分研究生物的基因功能注释提供了实用方案。
中文摘要 AI 辅助
非模式生物的基因功能注释仍是计算生物学领域的重大挑战，20%-70%的测序基因缺乏已表征功能。传统基于同源性的方法成本较高且高度依赖高序列相似性。本研究提出Homo-RAG，一种基于大语言模型的基因功能预测框架，整合了同源性引导的多跳检索与证据感知排序。该框架利用斑马鱼与人类直系同源物之间的生物学关系，通过混合密集检索与词汇检索从ZFIN、UniProt和PubMed中引导证据获取。证据置信度评分（ECS）整合语义相关性、实体匹配、同源性信息、来源可靠性及文献关联信号，以优化检索证据的排序。对150个查询及7200份检索文档的广泛评估显示，当证据权重参数λ=0.50时，NDCG@10提升至0.9879，MRR达0.99，且为99.33%的查询检索到相关证据；此外，80%的检索文档为查询专属，表明证据质量是对检索相关性的补充而非替代。这些发现确立了Homo-RAG为针对未充分研究生物进行可靠、基于证据的基因功能预测的实用且稳健的框架，该研究解决了传统注释流程的重要局限，同时为证据特征与归因机制的未来改进指明了方向。
英文摘要
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.
发表机构
- American International University-Bangladesh（孟加拉国国际大学）
机构由 AI 辅助整理，请以论文原文为准。
