LLMBDC：面向生物域的基因本体聚类语言模型

中文摘要 AI 辅助

基因本体（Gene Ontology, GO）富集分析是将大规模基因组数据转化为生物学见解的基础工具，但通常会产生数百个冗余术语，掩盖了总体主题。现有的汇总工具依赖固定相似度指标（如REVIGO、GOSemSim、clusterProfiler::simplify()）、基因重叠度量（如Metascape）或静态层次映射（如GO-slim），因此无法融入生物学背景。人工整理可提供背景感知的分组，但具有主观性且劳动强度大。需要一种可扩展的、背景感知的框架，将GO术语聚类为可解释的高阶生物域。本文提出LLMBDC（Large Language Model for Biological Domains Oriented Clustering of Gene Ontology），这是一种无需训练的框架，利用大型语言模型（LLM）的零样本语义推理及置信度评分，仅通过推理时的本体信息将GO术语聚类为生物域（BioDomains）。在阿尔茨海默病（Alzheimer's disease, AD）和脆性X综合征（Fragile X syndrome, FXS）上针对包括SapBERT在内的6种基线方法进行基准测试，LLMBDC实现了显著更高的精度、召回率和聚类性能。与真实标注相比，LLMBDC在AD上的调整兰德指数（Adjusted Rand Index, ARI）较REVIGO从9.7%提升至73.3%，标准化互信息（Normalized Mutual Information, NMI）从59.9%提升至73.4%；在FXS上ARI从15.7%提升至66.6%，NMI从66.0%提升至79.5%。柯西组合检验进一步证实，聚合后的生物域保留了具有统计学意义的功能信号。LLMBDC为GO富集结果的背景感知、系统层面解读提供了一种可扩展、可复现且可解释的途径，同时保留了生物学特异性。

英文摘要

Gene Ontology (GO) enrichment analysis is a foundational tool for translating large-scale genomic data into biological insights, but typically yields hundreds of redundant terms that obscure overarching themes. Existing summarization tools rely on fixed similarity metrics (REVIGO, GOSemSim, clusterProfiler::simplify()), gene-overlap measures (Metascape), or static hierarchy mappings (GO-slim), and therefore cannot incorporate biological context. Manual curation provides context-aware grouping but is subjective and labor-intensive. A scalable, context-aware framework is needed to cluster GO terms into interpretable higher-order biological domains. Here we present LLMBDC (Large Language Model for Biological Domains Oriented Clustering of Gene Ontology), a training-free framework that leverages zero-shot semantic reasoning of LLMs with confidence scoring to cluster GO terms into BioDomains using only ontology information at inference time. Benchmarked across Alzheimer's disease (AD) and Fragile X syndrome (FXS) against six baseline methods including SapBERT, LLMBDC achieved substantially higher precision, recall, and clustering performance. Against ground-truth annotations, LLMBDC improved ARI from 9.7% to 73.3% (AD) and from 15.7% to 66.6% (FXS) over REVIGO, with corresponding NMI gains from 59.9% to 73.4% (AD) and 66.0% to 79.5% (FXS). A Cauchy combination test further confirmed that aggregated BioDomains retained statistically significant functional signals. LLMBDC provides a scalable, reproducible, and interpretable route to context-aware, system-level interpretation of GO enrichment results while preserving biological specificity.

发表机构

• Emory University（埃默里大学）

机构由 AI 辅助整理，请以论文原文为准。