作者：Guangcan Li, Shuyuan Zheng, Jiayao Liu, Shigeto Seno, Makoto Onizuka
来源：arXiv cs.DB（数据库）
正文
Computer Science > Information Retrieval
arXiv:2610.30710v1 (cs)
[Submitted on 22 Oct 2026]
Title:Automated Enrichment Analysis with GraphRAG: Efficient Multi-Database ORA Extraction and Biological Interpretation
Authors:Guangcan Li, Shuyuan Zheng, Jiayao Liu, Shigeto Seno, Makoto Onizuka
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Abstract:Over-representation analysis (ORA) is widely used to interpret gene-expression studies, but its outputs are often fragmented across gene-set databases and presented as flat term lists that obscure gene-level links among related enrichment signals. We propose Graph-Based Over-Representation Retrieval-Augmented Generation (GBOR-RAG), an integrated and traceable framework for ORA interpretation. GBOR-RAG represents heterogeneous gene-set databases as a unified term–gene knowledge graph, where shared genes connect related terms across sources. After graph-based ORA scoring, the framework retrieves a compact connected evidence subgraph that preserves both cross-database relationships and gene-level support. The subgraph conditions an LLM to generate biological interpretations, and we introduce a claim-level faithfulness metric to assess whether generated claims are supported by explicit evidence. Experiments show that graph-based ORA preserves conventional ORA results with high consistency (mean Pearson $r=0.997$) while reducing query time by a median of $37\times$ (up to $48\times$). The retrieved subgraphs average 36.8 nodes and achieve the highest overall integration score (4.12), and GBOR-RAG attains the highest claim-level faithfulness (0.90) among the compared systems. Adding a PubMed literature layer further improves the overall interpretation quality from 4.17 to 4.75.
Comments:
Accepted to the 2026 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2026)
Subjects:
Information Retrieval (cs.IR); Databases (cs.DB)
Cite as:
arXiv:2610.30710 [cs.IR]
(or
arXiv:2610.30710v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2610.30710
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arXiv-issued DOI via DataCite (pending registration)
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信息检索 · 数据库
由「前沿雷达」于 2026-10-22 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。
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