Abstract BackgroundAnnotating scientific literature with ontology concepts is a critical task in biology and several other domains for knowledge discovery. Ontology based annotations can power large-scale comparative analyses in a wide range of applications ranging from evolutionary phenotypes to rare human diseases to the study of protein functions. Computational methods that can tag scientific text with ontology terms have included lexical/syntactic methods, traditional machine learning, and most recently, deep learning. ResultsHere, we present state of the art deep learning architectures based on Gated Recurrent Units for annotating text with ontology concepts. We use the Colorado Richly Annotated Full Text Corpus (CRAFT) as a gold standard for training and testing. We explore a number of additional information sources including NCBI’s BioThesauraus and Unified Medical Language System (UMLS) to augment information from CRAFT for increasing prediction accuracy. Our best model results in a 0.84 F1 and semantic similarity. ConclusionThe results shown here underscore the impact for using deep learning architectures for automatically recognizing ontology concepts from literature. The augmentation of the models with biological information beyond that present in the gold standard corpus shows a distinct improvement in prediction accuracy. 
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                            Ontology-Powered Boosting for Improved Recognition of Ontology Concepts from Biological Literature [Ontology-Powered Boosting for Improved Recognition of Ontology Concepts from Biological Literature]
                        
                    - Award ID(s):
- 1942727
- PAR ID:
- 10463374
- Date Published:
- Journal Name:
- 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023)
- Volume:
- 3
- Issue:
- Bioinformatics
- Page Range / eLocation ID:
- 80 to 90
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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