PlantConnectome: knowledge networks encompassing >100,000 plant article abstracts
One of the main quests of plant biology is understanding how genes and metabolites work together to form complex networks that drive plant growth, development, and responses to environmental stimuli. However, the ever-growing volume and diversity of scientific literature make it increasingly challenging to stay current with the latest advances in gene function studies. Here, we tackle the challenge by deploying the text-mining capacities of large language models to process over 71,000 plant biology abstracts. Our approach unveiled nearly 5 million functional relationships between a wide array of biological entities--genes, metabolites, tissues, and others--with a high accuracy of over 85%. We encapsulated these findings in PlantConnectome, a user-friendly database, and demonstrated its diverse utility by providing insights into gene regulatory networks, protein-protein interactions, and stress responses. We believe this innovative use of AI in the life sciences will allow plant scientists to keep up to date with the rapidly growing corpus of scientific literature. PlantConnectome is available at https://plant.connectome.tools/.