Large language models in plant biology.

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Large language models (LLMs), exemplified by ChatGPT, demonstrate significant capabilities beyond human language processing.
• These models excel at analyzing complex sequential biological data, including DNA, protein sequences, and gene expression patterns.
• Foundation models derived from LLMs offer versatile predictive power for understanding cellular system states.

Purpose of the Study:

• To review the diverse types of large language models (LLMs).
• To highlight recent advancements and applications of LLMs in biological research.
• To explore the untapped potential and deployment strategies for LLMs within the plant kingdom.

Main Methods:

• Review of existing literature on large language models (LLMs).
• Analysis of LLM architectures and their adaptation for biological data.
• Case studies showcasing LLM applications in various biological domains.
• Discussion of challenges and opportunities for implementing LLMs in plant science.

Main Results:

• LLMs can effectively identify intricate patterns in biological sequence data.
• Repurposed LLM foundation models serve as potent, multipurpose prediction tools for cellular systems.
• Current LLM adoption in plant biology is limited, indicating a significant research gap.
• The review provides a framework for applying LLMs to plant-specific biological questions.

Conclusions:

• Large language models (LLMs) represent a transformative technology with broad applicability in biological sciences.
• Significant opportunities exist for leveraging LLMs to advance plant biology research and applications.
• Further research and development are needed to fully realize the potential of LLMs in the plant kingdom.