From text to traits: exploring the role of large language models in plant breeding.

Abstract

Modern plant breeders regularly deal with the intricate patterns within biological data in order to better understand the biological background behind a trait of interest and speed up the breeding process. Recently, Large Language Models (LLMs) have gained widespread adoption in everyday contexts, showcasing remarkable capabilities in understanding and generating human-like text. By harnessing the capabilities of LLMs, foundational models can be repurposed to uncover intricate patterns within biological data, leading to the development of robust and flexible predictive tools that provide valuable insights into complex plant breeding systems. Despite the significant progress made in utilizing LLMs in various scientific domains, their adoption within plant breeding remains unexplored, presenting a significant opportunity for innovation. This review paper explores how LLMs, initially designed for natural language tasks, can be adapted to address specific challenges in plant breeding, such as identifying novel genetic interactions, predicting performance of a trait of interest, and well-integrating diverse datasets such as multi-omics, phenotypic, and environmental sources. Compared to conventional breeding methods, LLMs offer the potential to enhance the discovery of genetic relationships, improve trait prediction accuracy, and facilitate informed decision-making. This review aims to bridge this gap by highlighting current advancements, challenges, and future directions for integrating LLMs into plant breeding, ultimately contributing to sustainable agriculture and improved global food security.