Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy.

ABSTRACT: Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease and lacks guaranteed pharmacological therapeutic options. In this study, we applied a network-based framework for comprehensively identifying candidate flavonoids for the prevention and/or treatment of NAFLD. Flavonoid-target interaction information was obtained from combining experimentally validated data and results obtained using a recently developed machine-learning model, AI-DTI. Flavonoids were then prioritized by calculating the network proximity between flavonoid targets and NAFLD-associated proteins. The preventive effects of the candidate flavonoids were evaluated using FFA-induced hepatic steatosis in HepG2 and AML12 cells. We reconstructed the flavonoid-target network and found that the nu

SUBMITTER: Lee WY 

PROVIDER: S-EPMC9204489 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature