{"title":"植物生物学中的大型语言模型","authors":"Hilbert Yuen In Lam, Xing Er Ong, Marek Mutwil","doi":"10.1016/j.tplants.2024.04.013","DOIUrl":null,"url":null,"abstract":"<p><p>Large language models (LLMs), such as ChatGPT, have taken the world by storm. However, LLMs are not limited to human language and can be used to analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multipurpose prediction tools able to predict the state of cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.</p>","PeriodicalId":23264,"journal":{"name":"Trends in Plant Science","volume":" ","pages":"1145-1155"},"PeriodicalIF":17.3000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Large language models in plant biology.\",\"authors\":\"Hilbert Yuen In Lam, Xing Er Ong, Marek Mutwil\",\"doi\":\"10.1016/j.tplants.2024.04.013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Large language models (LLMs), such as ChatGPT, have taken the world by storm. However, LLMs are not limited to human language and can be used to analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multipurpose prediction tools able to predict the state of cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.</p>\",\"PeriodicalId\":23264,\"journal\":{\"name\":\"Trends in Plant Science\",\"volume\":\" \",\"pages\":\"1145-1155\"},\"PeriodicalIF\":17.3000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Plant Science\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1016/j.tplants.2024.04.013\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"PLANT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Plant Science","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.tplants.2024.04.013","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/26 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
Large language models (LLMs), such as ChatGPT, have taken the world by storm. However, LLMs are not limited to human language and can be used to analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multipurpose prediction tools able to predict the state of cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.
期刊介绍:
Trends in Plant Science is the primary monthly review journal in plant science, encompassing a wide range from molecular biology to ecology. It offers concise and accessible reviews and opinions on fundamental plant science topics, providing quick insights into current thinking and developments in plant biology. Geared towards researchers, students, and teachers, the articles are authoritative, authored by both established leaders in the field and emerging talents.