Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications.

ABSTRACT: Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in structured databases and literature is often complementary. We developed a machine learning method that combines information from literature and databases to predict drug targets and indications. To effectively utilize information in published literature, we integrate knowledge graphs and published literature using named entity recognition and normalization before applying a machine learning model that utilizes the combination of graph and literature. We then use supervised machine learning to show the effects of combining features from biomedical k

SUBMITTER: Alshahrani M 

PROVIDER: S-EPMC8988936 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature