由植物蛋白酸化数据库和ChatGPT驱动的多模式知识扩展小程序
Chunhui Xu1,2, Yang Yu1,3, Govardhan Khadakkar3
1Bond Life Sciences Center, University of Missouri-Columbia, Columbia, MO, United States.
Frontiers in bioinformatics
|October 31, 2025
概括
我们将大型语言模型 (LLM) 与植物蛋白酸化数据库 (P3DB) 集成,以实现自然语言查询和视觉数据提取,提高生物知识库的可访问性.
科学领域:
- 生物信息学是一种生物信息学.
- 植物生物学 植物生物学
- 人工智能的人工智能
背景情况:
- 生物数据库具有严格的结构,限制了用户的探索.
- 植物酸化数据庞大,但在精心策划的数据库中代表性不足.
- 将大型语言模型 (LLM) 集成到生物信息学工具中是一项挑战.
研究的目的:
- 开发一个多式问答小程序,将ChatGPT-4o与植物蛋白酸化数据库 (P3DB) 集成在一起.
- 为了使自然语言查询和从植物酸化研究的科学数据中提取视觉数据.
- 提高用户可访问性,并扩大生物知识库的实用性.
主要方法:
- 开发了一个多式联机问答小程序,将ChatGPT-4o与P3DB连接起来.
- 实现了自然语言查询支持和动态提示表述.
- 探索了提示策略 (一步,两步,几次拍摄,图像裁剪) 以获得最佳准确性.
主要成果:
- 聊天GPT-P3DB集成支持对酸化事件,酶-基质关系和蛋白质-蛋白质相互作用的探索.
- 该小程序成功地通过图像解释从科学数据中提取调节途径和酸化标记.
- 经过测试的多式联络LLM (ChatGPT-5,Gemini 1.5) 显示了类似的能力.
结论:
- 聊天GPT-P3DB集成增强了用户对植物酸化数据的可访问性.
- 该系统可以从复杂的图形中提取视觉数据,扩展知识库的实用性.
- 开发的框架为生物知识发现提供了灵活和适应性的方法.
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