计算机科学
桥接(联网)
文档
阅读(过程)
深度学习
人工智能
理解力
过程(计算)
数据科学
生物医学文本挖掘
自然语言处理
文本挖掘
计算机网络
政治学
法学
程序设计语言
操作系统
作者
Zheni Zeng,Yuan Yao,Zhiyuan Liu,Maosong Sun
标识
DOI:10.1038/s41467-022-28494-3
摘要
To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from versatile reading on both molecule structure and biomedical text information, we propose a knowledgeable machine reading system that bridges both types of information in a unified deep-learning framework for comprehensive biomedical research assistance. We solve the problem that existing machine reading models can only process different types of data separately, and thus achieve a comprehensive and thorough understanding of molecule entities. By grasping meta-knowledge in an unsupervised fashion within and across different information sources, our system can facilitate various real-world biomedical applications, including molecular property prediction, biomedical relation extraction and so on. Experimental results show that our system even surpasses human professionals in the capability of molecular property comprehension, and also reveal its promising potential in facilitating automatic drug discovery and documentation in the future.
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