Large language models in plant biology

被引：21

作者：

Lam, Hilbert Yuen In ^{[1
]}

Ong, Xing Er ^{[1
]}

Mutwil, Marek ^{[1
]}

机构：

[1] Nanyang Technol Univ, Sch Biol Sci, 60 Nanyang Dr, Singapore 637551, Singapore

来源：

TRENDS IN PLANT SCIENCE | 2024年 / 29卷 / 10期

关键词：

AI; decoder; embedding; encoder; foundation model; large language models; transformer;

D O I：

10.1016/j.tplants.2024.04.013

中图分类号：

Q94 [植物学];

学科分类号：

071001 ;

摘要：

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.

引用

页码：1145 / 1155

页数：11

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