Mining Protein-Protein Interactions from GeneRIFs with OpenDMAP

  • Andrew D. Fox
  • William A. BaumgartnerJr.
  • Helen L. Johnson
  • Lawrence E. Hunter
  • Donna K. Slonim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6004)


We applied the OpenDMAP [1] and BioNLP-UIMA [2] NLP systems to the task of mining protein-protein interactions (PPIs) from GeneRIFs. Our goal was to assess and improve system performance on GeneRIF text. We identified several classes of errors in the system’s output on a training dataset (most notably difficulty recognizing protein complexes) and modified the system to improve performance based on these observations. To improve recognition of protein complex interactions, we implemented a new protein-complex-resolution UIMA component. We added a custom entity identification engine that uses GeneRIF metadata to annotate proteins that may have been missed by the other engines. These changes simultaneously improved both recall and precision, resulting in an overall improvement in F-measure (from 0.23 to 0.48). Results confirm that the targeted enhancements described here lead to a substantial improvement in performance.

Availability: Annotated data sets and source code for the new UIMA components can be found at


Concept Recognition Metadata Tagger Gene Ontology Annota Entrez Gene Entry Interaction Pair Subtask 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrew D. Fox
    • 1
  • William A. BaumgartnerJr.
    • 2
  • Helen L. Johnson
    • 2
  • Lawrence E. Hunter
    • 2
  • Donna K. Slonim
    • 1
    • 3
  1. 1.Department of Computer ScienceTufts UniversityMedford
  2. 2.Center for Computational PharmacologyUniversity of Colorado School of MedicineAurora
  3. 3.Department of PathologyTufts University School of MedicineBoston

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