Abstract:

Background: For automated reading of scientific publications to
extract useful information about molecular mechanisms it is critical that
genes, proteins and other entities be correctly associated with uniform
identifiers, a process known as named entity linking or "grounding.'' Correct
grounding is essential for resolving relationships among mined information,
curated interaction databases, and biological datasets. The accuracy of this
process is largely dependent on the availability of machine-readable resources
associating synonyms and abbreviations commonly found in biomedical literature
with uniform identifiers.

Results: In a task involving automated reading of  ~215,000
articles using the REACH event extraction software we found that grounding was
disproportionately inaccurate for multi-protein families (e.g., "AKT") and
complexes with multiple subunits  (e.g."NF-kappaB'"). To address this
problem we constructed FamPlex, a manually curated resource defining protein
families and complexes as they are commonly encountered  in biomedical text. In
FamPlex the gene-level constituents of families and complexes are defined in a
flexible format allowing for multi-level, hierarchical membership. To create
FamPlex, text strings corresponding to entities were identified empirically
from literature and linked manually to uniform identifiers; these identifiers
were also mapped to equivalent entries in multiple related databases. FamPlex
also includes curated prefix and suffix patterns that improve named entity
recognition and event extraction.  Evaluation of REACH extractions on a test
corpus of ~54,000 articles showed that FamPlex significantly increased
grounding accuracy for families and complexes (from 15% to 71%). The
hierarchical organization of entities in FamPlex also made it possible to
integrate otherwise unconnected mechanistic information across families,
subfamilies, and individual proteins. Applications of FamPlex to the TRIPS/DRUM
reading system and the Biocreative VI Bioentity Normalization Task dataset
demonstrated the utility of FamPlex in other settings.

Conclusion: FamPlex is an effective resource for improving named
entity recognition, grounding, and relationship resolution in automated reading
of biomedical text. The content in FamPlex is available in both tabular and
Open Biomedical Ontology formats at
https://github.com/sorgerlab/famplex under the Creative Commons CC0
license and has been integrated into the TRIPS/DRUM and REACH reading systems.