Abstract
Abstract—Proteins and their interactions govern virtually all cellular processes, such as regulation, signaling, metabolism, and
structure. Most experimental findings pertaining to such interactions are discussed in research papers, which, in turn, get curated by
protein interaction databases. Authors, editors, and publishers benefit from efforts to alleviate the tasks of searching for relevant
papers, evidence for physical interactions, and proper identifiers for each protein involved. The BioCreative II.5 community challenge
addressed these tasks in a competition-style assessment to evaluate and compare different methodologies, to make aware of the
increasing accuracy of automated methods, and to guide future implementations. In this paper, we present our approaches for proteinnamed
entity recognition, including normalization, and for extraction of protein-protein interactions from full text. Our overall goal is to
identify efficient individual components, and we compare various compositions to handle a single full-text article in between 10 seconds
and 2 minutes. We propose strategies to transfer document-level annotations to the sentence-level, which allows for the creation of a
more fine-grained training corpus; we use this corpus to automatically derive around 5,000 patterns. We rank sentences by relevance
to the task of finding novel interactions with physical evidence, using a sentence classifier built from this training corpus. Heuristics for
paraphrasing sentences help to further remove unnecessary information that might interfere with patterns, such as additional
adjectives, clauses, or bracketed expressions. In BioCreative II.5, we achieved an f-score of 22 percent for finding protein interactions,
and 43 percent for mapping proteins to UniProt IDs; disregarding species, f-scores are 30 percent and 55 percent, respectively. On
average, our best-performing setup required around 2 minutes per full text. All data and pattern sets as well as Java classes that
extend third-party software are available as supplementary information (see Appendix).
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