Title: An AI-powered Knowledge Hub for Potato Functional Genomics Show Chinese title

Title: 一个以人工智能为核心的马铃薯功能基因组学知识中心

Abstract: Potato functional genomics lags due to unsystematic gene information curation, gene identifier inconsistencies across reference genome versions, and the increasing volume of research publications. To address these limitations, we developed the Potato Knowledge Hub (http://www.potato-ai.top), leveraging Large Language Models (LLMs) and a systematically curated collection of over 3,200 high-quality potato research papers spanning over 120 years. This platform integrates two key modules: a functional gene database containing 2,571 literature-reported genes, meticulously mapped to the latest DMv8.1 reference genome with resolved nomenclature discrepancies and links to original publications; and a potato knowledge base. The knowledge base, built using a Retrieval-Augmented Generation (RAG) architecture, accurately answers research queries with literature citations, mitigating LLM "hallucination." Users can interact with the hub via a natural language AI agent, "Potato Research Assistant," for querying specialized knowledge, retrieving gene information, and extracting sequences. The continuously updated Potato Knowledge Hub aims to be a comprehensive resource, fostering advancements in potato functional genomics and supporting breeding programs. Show Chinese abstract

Abstract: 由于基因信息整理缺乏系统性、不同参考基因组版本的基因标识符不一致以及研究论文数量不断增加，马铃薯功能基因组学的研究进展受到限制。 为了解决这些局限性，我们开发了马铃薯知识中心（http://www.potato-ai.top），利用大型语言模型（LLMs）和经过系统整理的超过3,200篇高质量马铃薯研究论文（覆盖超过120年的时间）。 该平台集成了两个关键模块：一个包含2,571个文献报道基因的功能基因数据库，这些基因被仔细映射到最新的DMv8.1参考基因组上，并解决了命名上的不一致性，同时链接到原始出版物；还有一个马铃薯知识库。 知识库采用检索增强生成（RAG）架构构建，能够准确回答研究问题并提供文献引用，从而减轻了LLM“幻觉”问题。 用户可以通过自然语言AI代理“马铃薯研究助手”，与知识中心互动，查询专业知识、检索基因信息并提取序列。 不断更新的马铃薯知识中心旨在成为一个全面的资源，推动马铃薯功能基因组学的进步，并支持育种计划。