Extracting chemical-protein interactions from biomedical literature via granular attention based recurrent neural networks.
Journal:
Computer methods and programs in biomedicine
Published Date:
Apr 30, 2019
Abstract
BACKGROUND AND OBJECTIVE: The extraction of interactions between chemicals and proteins from biomedical literature is important for many biomedical tasks such as drug discovery and precision medicine. In the existing systems, the methods achieving competitive results are combined of several models or implemented in multi-stage, and they are challenged by high cost because numerous external features are employed. These problems can be avoided by deep learning algorithms, but the performance of the deep learning based models is limited by inadequate exploration of the information. Our goal is to devise a system to improve the performance of the automatic extraction between chemical entities and protein entities from biomedical literature.