KGG: a fully automated workflow for creating disease-specific knowledge graphs.

ABSTRACT:

Motivation

Knowledge graphs (KGs) in life sciences have become an important application of systems biology as they delineate complex biological and pathophysiological phenomena. They are composed of biological and chemical entities represented with standard ontologies to comply with Findable, Accessible, Interoperable and Reusable (FAIR) principles. Alongside serving as a graph database, KGs hold the potential to address complex scientific queries and facilitate downstream analyses. However, the process of constructing KGs is expensive and time consuming as it primarily relies on manual curation from published literature and experimental data. The existing text-mining workflows are still in their infancy and fail to achieve the accuracy and reliability of manual curation.

Result

SUBMITTER: Karki R 

PROVIDER: S-EPMC12270262 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature