Modular semantic tagging of medline abstracts and its use in inferring regulatory networks

Marc Verhagen, James Pustejovsky, Ronald Taylor, Antonio Sanfilippo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe MedstractPlus, a resource for mining relations from the Medline bibliographic database that is currently under construction. It was built on the remains of Medstract, a previously created resource that included a biorelation server and an acronym database. MedstractPlus uses simple and scalable natural language processing modules to structure text, is designed with reusability and extendibility in mind, and adheres to the philosophy of the Linguistic Annotation Framework. We show how MedstractPlus has been used to provide seeds for a novel approach to inferring transcriptional regulatory networks from gene expression data.

Original languageEnglish
Title of host publicationProceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011
Pages498-505
Number of pages8
DOIs
Publication statusPublished - 21 Nov 2011
Event5th Annual IEEE International Conference on Semantic Computing, ICSC 2011 - Palo Alto, CA, United States
Duration: 18 Sep 201121 Sep 2011

Publication series

NameProceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011

Conference

Conference5th Annual IEEE International Conference on Semantic Computing, ICSC 2011
CountryUnited States
CityPalo Alto, CA
Period18/9/1121/9/11

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

Cite this

Verhagen, M., Pustejovsky, J., Taylor, R., & Sanfilippo, A. (2011). Modular semantic tagging of medline abstracts and its use in inferring regulatory networks. In Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011 (pp. 498-505). [6061506] (Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011). https://doi.org/10.1109/ICSC.2011.78