Large Language Models in Plant Biology
It addresses the problem of leveraging advanced AI models for biological research, particularly in plant science, but is incremental as it reviews existing methods rather than introducing new ones.
This review explores the application of Large Language Models (LLMs) to analyze sequential biological data like DNA and proteins, highlighting their potential as multi-purpose prediction tools for explaining cellular systems, with a focus on adapting these models for plant biology where they are not yet widely used.
Large Language Models (LLMs), such as ChatGPT, have taken the world by storm and have passed certain forms of the Turing test. However, LLMs are not limited to human language and 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, multi-purpose prediction tools able to explain 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.