Abstract
With the increasing amount of biomedical literature, there is a need for automatic extraction of information to support biomedical researchers. Due to incomplete biomedical information databases, the extraction cannot be done straightforward using dictionaries, so several approaches using contextual rules and machine learning have previously been proposed. Our work is inspired by the previous approaches, but is novel in the sense that it combines Google and Gene Ontology for annotating protein interactions. We got promising empirical results – 57.5% terms as valid GO annotations, and 16.9% protein names in the answers provided by our system gProt. The total error-rate was 25.6% consisting mainly of overly general answers and syntactic errors, but also including semantic errors, other biological entities (than proteins and GO-terms) and false information sources.
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Sætre, R. et al. (2005). gProt: Annotating Protein Interactions Using Google and Gene Ontology. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_166
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DOI: https://doi.org/10.1007/11553939_166
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