STonKGs: a sophisticated transformer trained on biomedical text and knowledge graphs.

ABSTRACT:

Motivation

The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models. However, representations based on a single modality are inherently limited.

Results

To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs (KGs). This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations in a s

SUBMITTER: Balabin H 

PROVIDER: S-EPMC8896635 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature