miRKatAI：一个用于microRNA研究的集成数据库与多智能体AI系统
miRKatAI: An Integrated Database and Multi-agent AI system for microRNA Research
AI总结：
本文提出miRKat Suite，包含整合多源数据的miR-靶标相互作用数据库miRKatDB，以及基于LLMs和LangGraph的多智能体系统miRKatAI，通过自然语言查询与可视化加速microRNA研究。
摘要：
微小RNA（miRs）是基因表达的稳健调节因子，参与大多数生物学过程。microRNAs主要在转录后下调基因的表达，且每个miR预计靶向数百个基因。准确识别和注释miR-mRNA靶标相互作用对于理解miRs功能及其治疗潜力至关重要。然而，由于miRs与其靶标的不完全互补性，计算靶标预测具有挑战性，且实验数据不断增长的规模和异质性给跨生物学背景访问、整合和分析miR-靶标相互作用信息带来了挑战。这催生了对集成资源和智能查询工具的需求。我们提出了miRKat Suite，包含miRKatDB（一个全面且经过人工筛选的数据库，涵盖预测和验证的miR-靶标相互作用及相关注释）以及miRKatAI（一个由大型语言模型（LLMs）和LangGraph驱动的多智能体系统）。miRKatDB整合了来自多个公开可用来源的数据，为miR研究提供了全面的基础，包括miR靶基因和先前报道的组织表达水平变化。miRKatAI提供自然语言接口以对miRKatDB进行复杂查询，促进从该领域既有来源进行有依据的信息检索，并支持基础数据可视化。miRKat Suite旨在通过简化数据访问、增强探索性分析和支持假设生成来加速miR研究。
英文摘要：
MicroRNAs (miRs) are robust regulators of gene expression, implicated in most biological processes. microRNAs predominantly downregulate the expression of genes post-transcriptionally and each miR is predicted to target several hundred genes. The accurate identification and annotation of miR-mRNA target interactions is central to understanding miRs function and their therapeutic potential. However, computational target prediction is challenging due to imperfect complementarity of miRs with their targets and the growing volume and heterogeneity of experimental data present challenges in accessing, integrating, and analysing miR-target interaction information across biological contexts. This creates a need for integrated resources and intelligent query tools. We present the miRKat Suite, comprising miRKatDB, a comprehensive, curated database of predicted and validated miR-target interactions and associated annotations, and miRKatAI, a multi-agent system powered by large language models (LLMs) and LangGraph. miRKatDB integrates data from multiple publicly available sources, providing a comprehensive foundation for miR studies, including miR target genes and changes in levels of tissue expression previously reported. miRKatAI offers a natural language interface for complex querying of miRKatDB, facilitates grounded information retrieval from established sources in the field, and supports basic data visualisation. The miRKat Suite aims to accelerate miR research by streamlining data access, enhancing exploratory analysis, and supporting hypothesis generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
