使用 ChatGPT 的生物信息学和生物医学信息学：一年回顾。

IF 0.6 4区生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Quantitative Biology Pub Date : 2024-12-01 Epub Date: 2024-06-27 DOI:10.1002/qub2.67

Jinge Wang, Zien Cheng, Qiuming Yao, Li Liu, Dong Xu, Gangqing Hu

{"title":"使用 ChatGPT 的生物信息学和生物医学信息学：一年回顾。","authors":"Jinge Wang, Zien Cheng, Qiuming Yao, Li Liu, Dong Xu, Gangqing Hu","doi":"10.1002/qub2.67","DOIUrl":null,"url":null,"abstract":"The year 2023 marked a significant surge in the exploration of applying large language model chatbots, notably Chat Generative Pre-trained Transformer (ChatGPT), across various disciplines. We surveyed the application of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.","PeriodicalId":45660,"journal":{"name":"Quantitative Biology","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446534/pdf/","citationCount":"0","resultStr":"{\"title\":\"Bioinformatics and biomedical informatics with ChatGPT: Year one review.\",\"authors\":\"Jinge Wang, Zien Cheng, Qiuming Yao, Li Liu, Dong Xu, Gangqing Hu\",\"doi\":\"10.1002/qub2.67\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The year 2023 marked a significant surge in the exploration of applying large language model chatbots, notably Chat Generative Pre-trained Transformer (ChatGPT), across various disciplines. We surveyed the application of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.\",\"PeriodicalId\":45660,\"journal\":{\"name\":\"Quantitative Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11446534/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quantitative Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1002/qub2.67\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/27 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/qub2.67","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/27 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

2023 年，大型语言模型聊天机器人（尤其是聊天生成预训练转换器（ChatGPT））在各学科中的应用探索出现了显著的增长。我们调查了 ChatGPT 在生物信息学和生物医学信息学中的全年应用情况，涵盖了omics、遗传学、生物医学文本挖掘、药物发现、生物医学图像理解、生物信息学编程和生物信息学教育。我们的调查描述了该聊天机器人目前在生物信息学方面的优势和局限性，并对未来发展的潜在途径提出了见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Bioinformatics and biomedical informatics with ChatGPT: Year one review.

The year 2023 marked a significant surge in the exploration of applying large language model chatbots, notably Chat Generative Pre-trained Transformer (ChatGPT), across various disciplines. We surveyed the application of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.

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

Quantitative Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

5.00

自引率

3.20%

发文量

264

期刊介绍： Quantitative Biology is an interdisciplinary journal that focuses on original research that uses quantitative approaches and technologies to analyze and integrate biological systems, construct and model engineered life systems, and gain a deeper understanding of the life sciences. It aims to provide a platform for not only the analysis but also the integration and construction of biological systems. It is a quarterly journal seeking to provide an inter- and multi-disciplinary forum for a broad blend of peer-reviewed academic papers in order to promote rapid communication and exchange between scientists in the East and the West. The content of Quantitative Biology will mainly focus on the two broad and related areas: ·bioinformatics and computational biology, which focuses on dealing with information technologies and computational methodologies that can efficiently and accurately manipulate –omics data and transform molecular information into biological knowledge. ·systems and synthetic biology, which focuses on complex interactions in biological systems and the emergent functional properties, and on the design and construction of new biological functions and systems. Its goal is to reflect the significant advances made in quantitatively investigating and modeling both natural and engineered life systems at the molecular and higher levels. The journal particularly encourages original papers that link novel theory with cutting-edge experiments, especially in the newly emerging and multi-disciplinary areas of research. The journal also welcomes high-quality reviews and perspective articles.