QuST-LLM:整合大型语言模型进行综合空间转录组学分析

Chao Hui Huang
{"title":"QuST-LLM:整合大型语言模型进行综合空间转录组学分析","authors":"Chao Hui Huang","doi":"arxiv-2406.14307","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce QuST-LLM, an innovative extension of QuPath that\nutilizes the capabilities of large language models (LLMs) to analyze and\ninterpret spatial transcriptomics (ST) data. This tool effectively simplifies\nthe intricate and high-dimensional nature of ST data by offering a\ncomprehensive workflow that includes data loading, region selection, gene\nexpression analysis, and functional annotation. QuST-LLM employs LLMs to\ntransform complex ST data into understandable and detailed biological\nnarratives based on gene ontology annotations, thereby significantly improving\nthe interpretability of ST data. Consequently, users can interact with their\nown ST data using natural language. Hence, QuST-LLM provides researchers with a\npotent functionality to unravel the spatial and functional complexities of\ntissues, fostering novel insights and advancements in biomedical research.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"QuST-LLM: Integrating Large Language Models for Comprehensive Spatial Transcriptomics Analysis\",\"authors\":\"Chao Hui Huang\",\"doi\":\"arxiv-2406.14307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we introduce QuST-LLM, an innovative extension of QuPath that\\nutilizes the capabilities of large language models (LLMs) to analyze and\\ninterpret spatial transcriptomics (ST) data. This tool effectively simplifies\\nthe intricate and high-dimensional nature of ST data by offering a\\ncomprehensive workflow that includes data loading, region selection, gene\\nexpression analysis, and functional annotation. QuST-LLM employs LLMs to\\ntransform complex ST data into understandable and detailed biological\\nnarratives based on gene ontology annotations, thereby significantly improving\\nthe interpretability of ST data. Consequently, users can interact with their\\nown ST data using natural language. Hence, QuST-LLM provides researchers with a\\npotent functionality to unravel the spatial and functional complexities of\\ntissues, fostering novel insights and advancements in biomedical research.\",\"PeriodicalId\":501070,\"journal\":{\"name\":\"arXiv - QuanBio - Genomics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2406.14307\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2406.14307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在本文中,我们介绍了 QuST-LLM,它是 QuPath 的创新扩展,利用大型语言模型(LLM)的功能来分析和解释空间转录组学(ST)数据。该工具提供了一个全面的工作流程,包括数据加载、区域选择、基因表达分析和功能注释,从而有效简化了空间转录组学数据的复杂性和高维性。QuST-LLM 利用 LLM 将复杂的 ST 数据转化为基于基因图谱注释的可理解的详细生物学叙述,从而大大提高了 ST 数据的可解释性。因此,用户可以使用自然语言与自己的 ST 数据进行交互。因此,QuST-LLM 为研究人员提供了揭示问题的空间和功能复杂性的强大功能,促进了生物医学研究的新见解和新进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
QuST-LLM: Integrating Large Language Models for Comprehensive Spatial Transcriptomics Analysis
In this paper, we introduce QuST-LLM, an innovative extension of QuPath that utilizes the capabilities of large language models (LLMs) to analyze and interpret spatial transcriptomics (ST) data. This tool effectively simplifies the intricate and high-dimensional nature of ST data by offering a comprehensive workflow that includes data loading, region selection, gene expression analysis, and functional annotation. QuST-LLM employs LLMs to transform complex ST data into understandable and detailed biological narratives based on gene ontology annotations, thereby significantly improving the interpretability of ST data. Consequently, users can interact with their own ST data using natural language. Hence, QuST-LLM provides researchers with a potent functionality to unravel the spatial and functional complexities of tissues, fostering novel insights and advancements in biomedical research.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Allium Vegetables Intake and Digestive System Cancer Risk: A Study Based on Mendelian Randomization, Network Pharmacology and Molecular Docking wgatools: an ultrafast toolkit for manipulating whole genome alignments Selecting Differential Splicing Methods: Practical Considerations Advancements in colored k-mer sets: essentials for the curious Advancements in practical k-mer sets: essentials for the curious
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1