Aggregating automatically extracted regulatory pathway relations.
Automatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. While several biomedical relation extraction systems have been created and tested, little work has been done to meaningfully organize the extracted relations. Organizational processes should consolidate multiple references to the same objects over various levels of granularity, connect those references to other resources, and capture contextual information. We propose a feature decomposition approach to relation aggregation to support a five-level aggregation framework. Our BioAggregate tagger uses this approach to identify key features in extracted relation name strings. We show encouraging feature assignment accuracy and report substantial consolidation in a network of extracted relations.
Marshall
Byron
B
Oregon State University, Corvallis 97331, USA. byron.marshall@bus.oregonstate.edu
Su
Hua
H
McDonald
Daniel
D
Eggers
Shauna
S
Chen
Hsinchun
H
D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D020543:Proteome / Q000378:metabolism*; D011642:Publications; D015398:Signal Transduction / Q000502:physiology*; D018875:Vocabulary, Controlled
D020543:Proteome