Abstract
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 http://bcb.cs.tufts.edu/GeneRIFs/
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Fox, A.D., Baumgartner, W.A., Johnson, H.L., Hunter, L.E., Slonim, D.K. (2010). Mining Protein-Protein Interactions from GeneRIFs with OpenDMAP. In: Blaschke, C., Shatkay, H. (eds) Linking Literature, Information, and Knowledge for Biology. Lecture Notes in Computer Science(), vol 6004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13131-8_6
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DOI: https://doi.org/10.1007/978-3-642-13131-8_6
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