He Fei, Liu Kai, Yang Zhiyuan, Hannink Mark, Hammer Richard D, Popescu Mihail, Xu Dong
School of Information Science and Technology, Northeast Normal University, Changchun, Jilin Province, China.
Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, USA.
Med Rev (2021). 2023 Jun 27;3(3):200-204. doi: 10.1515/mr-2023-0011. eCollection 2023 Jun.
The biomedical literature is a vast and invaluable resource for biomedical research. Integrating knowledge from the literature with biomedical data can help biological studies and the clinical decision-making process. Efforts have been made to gather information from the biomedical literature and create biomedical knowledge bases, such as KEGG and Reactome. However, manual curation remains the primary method to retrieve accurate biomedical entities and relationships. Manual curation becomes increasingly challenging and costly as the volume of biomedical publications quickly grows. Fortunately, recent advancements in Artificial Intelligence (AI) technologies offer the potential to automate the process of curating, updating, and integrating knowledge from the literature. Herein, we highlight the AI capabilities to aid in mining knowledge and building the knowledge base from the biomedical literature.
生物医学文献是生物医学研究的巨大且宝贵的资源。将文献中的知识与生物医学数据相结合有助于生物学研究和临床决策过程。人们已努力从生物医学文献中收集信息并创建生物医学知识库,如KEGG和Reactome。然而,人工编目仍然是检索准确生物医学实体和关系的主要方法。随着生物医学出版物数量的迅速增长,人工编目变得越来越具有挑战性且成本高昂。幸运的是,人工智能(AI)技术的最新进展为自动化文献知识的编目、更新和整合过程提供了潜力。在此,我们强调人工智能在辅助从生物医学文献中挖掘知识和构建知识库方面的能力。