Understanding protein interactions and pathway knowledge is crucial for
unraveling the complexities of living systems and investigating the underlying
mechanisms of biological functions and complex diseases. While existing
databases provide curated biological data from literature and other sources,
they are often incomplete and their maintenance is labor-intensive,
necessitating alternative approaches. In this study, we propose to harness the
capabilities of large language models to address these issues by automatically
extracting such knowledge from the relevant scientific literature. Toward this
goal, in this work, we investigate the effectiveness of different large
language models in tasks that involve recognizing protein interactions,
pathways, and gene regulatory relations. We thoroughly evaluate the performance
of various models, highlight the significant findings, and discuss both the
future opportunities and the remaining challenges associated with this
approach. The code and data are available at:
this https URL

利用大型语言模型自动从相关科学文献中提取知识，研究识别蛋白质相互作用、通路和基因调控关系的任务效果，并评估不同模型的性能，讨论了该方法的未来机遇和挑战。