Aggregating automatically extracted regulatory pathway relations

Byron Marshall, Hua Su, Daniel McDonald, Shauna Eggers, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)100-108
Number of pages9
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number1
StatePublished - Jan 2006


  • Knowledge representation
  • Regulatory pathway analysis
  • Relation parsing

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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