Abstract
Manual curation of biological databases is an expensive and labor-intensive process in Genomics and Systems Biology. We report the implem-entation of a state-of-the-art, rule-based Natural Language Processing system that creates computer-readable networks of regulatory interactions directly from abstracts and full-text papers. We evaluate its output against a manually-curated standard database, and test the possibilities and limitations of automatic and semi-automatic curation of the so-called biobibliome. We also propose a novel Regulatory Interaction Mining Markup Language suited for representing this data, useful both for biologists and for text-mining specialists.
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Rodríguez-Penagos, C., Salgado, H., Martínez-Flores, I., Collado-Vides, J. (2007). NLP-Based Curation of Bacterial Regulatory Networks. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2007. Lecture Notes in Computer Science, vol 4394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70939-8_51
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DOI: https://doi.org/10.1007/978-3-540-70939-8_51
Publisher Name: Springer, Berlin, Heidelberg
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