SCREENER: Streamlined collaborative learning of NER and RE model for discovering gene-disease relations.

ABSTRACT: Finding relations between genes and diseases is essential in developing a clinical diagnosis, treatment, and drug design for diseases. One successful approach for mining the literature is the document-based relation extraction method. Despite recent advances in document-level extraction of entity-entity, there remains a difficulty in understanding the relations between distant words in a document. To overcome the above limitations, we propose an AI-based text-mining model that learns the document-level relations between genes and diseases using an attention mechanism. Furthermore, we show that including a direct edge (DE) and indirect edges between genetic targets and diseases when training improves the model's performance. Such relation edges can be visualized as graphs, enhancing the int

SUBMITTER: Park M 

PROVIDER: S-EPMC10681162 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature