BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies.

ABSTRACT:

Background

Recent studies have proposed deep learning techniques, namely recurrent neural networks, to improve biomedical text mining tasks. However, these techniques rarely take advantage of existing domain-specific resources, such as ontologies. In Life and Health Sciences there is a vast and valuable set of such resources publicly available, which are continuously being updated. Biomedical ontologies are nowadays a mainstream approach to formalize existing knowledge about entities, such as genes, chemicals, phenotypes, and disorders. These resources contain supplementary information that may not be yet encoded in training data, particularly in domains with limited labeled data.

Results

We propose a new model to detect and classify relations in text, BO-LSTM, that takes ad

SUBMITTER: Lamurias A 

PROVIDER: S-EPMC6323831 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature