{"title":"利用大型语言模型扩展数据库和提取标记数据,推进植物代谢研究","authors":"Rachel Knapp, Braidon Johnson, Lucas Busta","doi":"10.1002/aps3.70007","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Premise</h3>\n \n <p>Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, have demonstrated that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been revealed with the advent of accessible large language models, which, even in their early stages, can distill structured data from the literature. This brings us closer to creating specialized databases that consolidate virtually all published knowledge on a topic.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Here, we first test different combinations of prompt engineering techniques and language models in the identification of validated enzyme–product pairs. Next, we evaluate the application of automated prompt engineering and retrieval-augmented generation to identify compound–species associations. Finally, we build and determine the accuracy of a multimodal language model–based pipeline that transcribes images of tables into machine-readable formats.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>When tuned for each specific task, these methods perform with high (80–90%) or modest (50%) accuracies for enzyme–product pair identification and table image transcription, but with lower false-negative rates than previous methods (decreasing from 55% to 40%) for compound–species pair identification.</p>\n </section>\n \n <section>\n \n <h3> Discussion</h3>\n \n <p>We enumerate several suggestions for researchers working with language models, among which is the importance of the user's domain-specific expertise and knowledge.</p>\n </section>\n </div>","PeriodicalId":8022,"journal":{"name":"Applications in Plant Sciences","volume":"13 4","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70007","citationCount":"0","resultStr":"{\"title\":\"Advancing plant metabolic research by using large language models to expand databases and extract labeled data\",\"authors\":\"Rachel Knapp, Braidon Johnson, Lucas Busta\",\"doi\":\"10.1002/aps3.70007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Premise</h3>\\n \\n <p>Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, have demonstrated that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been revealed with the advent of accessible large language models, which, even in their early stages, can distill structured data from the literature. This brings us closer to creating specialized databases that consolidate virtually all published knowledge on a topic.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>Here, we first test different combinations of prompt engineering techniques and language models in the identification of validated enzyme–product pairs. Next, we evaluate the application of automated prompt engineering and retrieval-augmented generation to identify compound–species associations. Finally, we build and determine the accuracy of a multimodal language model–based pipeline that transcribes images of tables into machine-readable formats.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>When tuned for each specific task, these methods perform with high (80–90%) or modest (50%) accuracies for enzyme–product pair identification and table image transcription, but with lower false-negative rates than previous methods (decreasing from 55% to 40%) for compound–species pair identification.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Discussion</h3>\\n \\n <p>We enumerate several suggestions for researchers working with language models, among which is the importance of the user's domain-specific expertise and knowledge.</p>\\n </section>\\n </div>\",\"PeriodicalId\":8022,\"journal\":{\"name\":\"Applications in Plant Sciences\",\"volume\":\"13 4\",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2025-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aps3.70007\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applications in Plant Sciences\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.70007\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PLANT SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applications in Plant Sciences","FirstCategoryId":"99","ListUrlMain":"https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.70007","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PLANT SCIENCES","Score":null,"Total":0}
Advancing plant metabolic research by using large language models to expand databases and extract labeled data
Premise
Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, have demonstrated that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been revealed with the advent of accessible large language models, which, even in their early stages, can distill structured data from the literature. This brings us closer to creating specialized databases that consolidate virtually all published knowledge on a topic.
Methods
Here, we first test different combinations of prompt engineering techniques and language models in the identification of validated enzyme–product pairs. Next, we evaluate the application of automated prompt engineering and retrieval-augmented generation to identify compound–species associations. Finally, we build and determine the accuracy of a multimodal language model–based pipeline that transcribes images of tables into machine-readable formats.
Results
When tuned for each specific task, these methods perform with high (80–90%) or modest (50%) accuracies for enzyme–product pair identification and table image transcription, but with lower false-negative rates than previous methods (decreasing from 55% to 40%) for compound–species pair identification.
Discussion
We enumerate several suggestions for researchers working with language models, among which is the importance of the user's domain-specific expertise and knowledge.
期刊介绍:
Applications in Plant Sciences (APPS) is a monthly, peer-reviewed, open access journal promoting the rapid dissemination of newly developed, innovative tools and protocols in all areas of the plant sciences, including genetics, structure, function, development, evolution, systematics, and ecology. Given the rapid progress today in technology and its application in the plant sciences, the goal of APPS is to foster communication within the plant science community to advance scientific research. APPS is a publication of the Botanical Society of America, originating in 2009 as the American Journal of Botany''s online-only section, AJB Primer Notes & Protocols in the Plant Sciences.
APPS publishes the following types of articles: (1) Protocol Notes describe new methods and technological advancements; (2) Genomic Resources Articles characterize the development and demonstrate the usefulness of newly developed genomic resources, including transcriptomes; (3) Software Notes detail new software applications; (4) Application Articles illustrate the application of a new protocol, method, or software application within the context of a larger study; (5) Review Articles evaluate available techniques, methods, or protocols; (6) Primer Notes report novel genetic markers with evidence of wide applicability.