Network for knowledge Organization (NEKO): An AI knowledge mining workflow for synthetic biology research

• 全文链接

知识组织网络（NEKO）：面向合成生物学研究的人工智能知识挖掘工作流

关键词：

来源：

Metabolic Engineering

全文链接：

//agri.nais.net.cn/topic/downloadFile/5f4366ce-b170-4bb1-9888-8cfd12063e0b

来源地址：

https://www.sciencedirect.com/science/article/pii/S1096717624001484?via%3Dihub

资源所属：

饲料用酶工程

类型：

学术文献

语种：

英语

原文发布日期：

2024-11-21

摘要：

Large language models (LLMs) can complete general scientific question-and-answer, yet they are constrained by their pretraining cut-off dates and lack the ability to provide specific, cited scientific knowledge. Here, we introduce Network for Knowledge Organization (NEKO), a workflow that uses LLM Qwen to extract knowledge through scientific literature text mining. When user inputs a keyword of interest, NEKO can generate knowledge graphs to link bioinformation entities and produce comprehensive summaries from PubMed search. NEKO significantly enhance LLM ability and has immediate applications in daily academic tasks such as education of young scientists, literature review, paper writing, experiment planning/troubleshooting, and new ideas/hypothesis generation. We exemplified this workflow's applicability through several case studies on yeast fermentation and cyanobacterial biorefinery. NEKO's output is more informative, specific, and actionable than GPT-4's zero-shot Q&A. NEKO offers flexible, lightweight local deployment options. NEKO democratizes artificial intelligence (AI) tools, making scientific foundation model more accessible to researchers without excessive computational power.