Artificial intelligence-driven plant bio-genomics research: a new era
2025
Lin Yang | Hao Wang | Meiling Zou | Haiwei Chai | Zhiqiang Xia
With the rapid development of artificial intelligence (AI) technology, particularly the emergence of large language models (LLMs) such as the GPT series, AI has been increasingly integrated into scientific research. These models exhibit robust cross-domain applicability by assimilating vast repositories of world knowledge and demonstrating proficiency in understanding and generating natural language. Leveraging the inherent similarities between genome sequences and natural language, this paper examines the recent advancements of AI in genomics. It elucidates the foundational principles of LLMs and their latest research developments in architectural design and functional analysis within the context of genomic data analysis. The paper also thoroughly explores the current challenges and prospective research directions. Despite the preliminary successes of LLMs in genomic research, significant obstacles remain in the integration of plant genomics with these models. This study highlights that LLMs offer innovative tools and perspectives for genomics research, extending to the fields of biology, agriculture, and even the study of tropical plants. Consequently, the effective utilization of AI technology by biologists to advance plant science has become a critical area of inquiry.
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