AD-GPT专用于阿尔茨海默病基因与脑区关系分析，提升医学信息检索精度。
AD-GPT: Large Language Models in Alzheimer's Disease
- 融合多种生物数据，用Llama3与BERT堆叠架构处理阿尔茨海默病相关基因。
- 在四个关键任务中均优于现有大模型，尤其在基因-脑区关联分析上表现突出。
- 适合神经科学与精准医疗研究者使用，助力阿尔茨海默病生物标志物发现。
大语言模型（LLMs）在医学信息检索中展现出强大能力，但在阿尔茨海默病（AD）等专业领域仍存在准确性和深度不足的问题。为填补这一空白，我们提出AD-GPT，一种针对阿尔茨海默病的生成式预训练变换器，旨在增强对相关基因与神经生物学信息的检索与分析能力。AD-GPT整合了潜在关联基因、分子遗传信息及与脑区相关的关键基因变异等多源生物数据。我们构建了基于Llama3与BERT的堆叠式大模型架构，专门优化四项核心任务：(1) 基因信息检索，(2) 基因-脑区关系评估，(3) 基因-阿尔茨海默病关系分析，(4) 脑区-阿尔茨海默病关系映射。对比实验表明，AD-GPT在各项任务中均表现出更高的精确度与可靠性，展现出作为专业化人工智能工具推动阿尔茨海默病研究与生物标志物发现的巨大潜力。
原文摘要 · Abstract (English)
Large language models (LLMs) have emerged as powerful tools for medical information retrieval, yet their accuracy and depth remain limited in specialized domains such as Alzheimer's disease (AD), a growing global health challenge. To address this gap, we introduce AD-GPT, a domain-specific generative pre-trained transformer designed to enhance the retrieval and analysis of AD-related genetic and neurobiological information. AD-GPT integrates diverse biomedical data sources, including potential AD-associated genes, molecular genetic information, and key gene variants linked to brain regions. We develop a stacked LLM architecture combining Llama3 and BERT, optimized for four critical tasks in AD research: (1) genetic information retrieval, (2) gene-brain region relationship assessment, (3) gene-AD relationship analysis, and (4) brain region-AD relationship mapping. Comparative evaluations against state-of-the-art LLMs demonstrate AD-GPT's superior precision and reliability across these tasks, underscoring its potential as a robust and specialized AI tool for advancing AD research and biomarker discovery.
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