生物医学数据分析中的大型语言模型:调查。

IF 7.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Journal of Biomedical and Health Informatics Pub Date : 2025-02-10 DOI:10.1109/JBHI.2025.3530794
Wei Lan;Zhentao Tang;Mingyang Liu;Qingfeng Chen;Wei Peng;Yi-ping Phoebe Chen;Yi Pan
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引用次数: 0

摘要

随着大语言模型(LLM)技术的迅速发展,LLM已成为生物医学数据分析研究中不可或缺的一支力量。然而,目前生物医学研究人员对LLM的了解有限。因此,迫切需要对法学硕士在生物医学数据分析中的应用进行总结。在此,我们对法学硕士在生物医学领域的最新研究工作进行综述。在这篇综述中,首先概述了法学硕士技术。然后,我们将讨论生物医学数据集和生物医学数据分析框架,然后详细分析LLM在基因组学、蛋白质组学、转录组学、放射组学、单细胞分析、医学文本和药物发现方面的应用。最后,讨论了LLM在生物医学数据分析方面面临的挑战。综上所述,这篇综述是针对对LLM技术感兴趣的研究人员,旨在帮助他们理解和应用LLM在生物医学数据分析研究。
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The Large Language Models on Biomedical Data Analysis: A Survey
With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research.
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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