A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals.

ABSTRACT: 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 ca

SUBMITTER: Zeng Z 

PROVIDER: S-EPMC8844428 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature