DeepKG: An End-to-End Deep Learning-Based Workflow for Biomedical Knowledge Graph Extraction, Optimization and Applications.

ABSTRACT: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research, etc. To improve the performance of DeepKG, a cascaded hybrid information extraction framework (CHIEF) is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144,900 COVID-19 scholarly full-te

SUBMITTER: Li Z 

PROVIDER: S-EPMC8689937 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature