Shan Chun Lim,Manoj Itharajula,Mads Harder Møller,Rohan Shawn Sunil,Kevin Fo,Yu Song Chuah,Herman Foo,Emilia Emmanuelle Davey,Melissa Fullwood,Guillaume Thibault,Marek Mutwil
{"title":"PlantConnectome:一个知识图谱数据库,包含bbb71,000篇植物文章。","authors":"Shan Chun Lim,Manoj Itharajula,Mads Harder Møller,Rohan Shawn Sunil,Kevin Fo,Yu Song Chuah,Herman Foo,Emilia Emmanuelle Davey,Melissa Fullwood,Guillaume Thibault,Marek Mutwil","doi":"10.1093/plcell/koaf169","DOIUrl":null,"url":null,"abstract":"One of the main quests in plant biology is understanding how gene products and metabolites work together to form complex networks that drive plant 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 functional genetics studies. Here, we tackled this challenge by deploying the text-mining capacities of large language models to process over 71,000 plant biology abstracts. Our approach presents nearly five million functional relationships between 2.4 million biological entities-genes or gene products, metabolites, tissues, and others-with a high accuracy of over 85%. We encapsulated these findings in the user-friendly database PlantConnectome 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/.","PeriodicalId":501012,"journal":{"name":"The Plant Cell","volume":"64 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PlantConnectome: a knowledge graph database encompassing >71,000 plant articles.\",\"authors\":\"Shan Chun Lim,Manoj Itharajula,Mads Harder Møller,Rohan Shawn Sunil,Kevin Fo,Yu Song Chuah,Herman Foo,Emilia Emmanuelle Davey,Melissa Fullwood,Guillaume Thibault,Marek Mutwil\",\"doi\":\"10.1093/plcell/koaf169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the main quests in plant biology is understanding how gene products and metabolites work together to form complex networks that drive plant 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 functional genetics studies. Here, we tackled this challenge by deploying the text-mining capacities of large language models to process over 71,000 plant biology abstracts. Our approach presents nearly five million functional relationships between 2.4 million biological entities-genes or gene products, metabolites, tissues, and others-with a high accuracy of over 85%. We encapsulated these findings in the user-friendly database PlantConnectome 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/.\",\"PeriodicalId\":501012,\"journal\":{\"name\":\"The Plant Cell\",\"volume\":\"64 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Plant Cell\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/plcell/koaf169\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Plant Cell","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/plcell/koaf169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PlantConnectome: a knowledge graph database encompassing >71,000 plant articles.
One of the main quests in plant biology is understanding how gene products and metabolites work together to form complex networks that drive plant 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 functional genetics studies. Here, we tackled this challenge by deploying the text-mining capacities of large language models to process over 71,000 plant biology abstracts. Our approach presents nearly five million functional relationships between 2.4 million biological entities-genes or gene products, metabolites, tissues, and others-with a high accuracy of over 85%. We encapsulated these findings in the user-friendly database PlantConnectome 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/.