Automating the Extraction of Essential Genes from Literature
The construction of repositories with curated information about gene essentiality for organisms of interest in Biotechnology is a very relevant task, mainly in the design of cell factories for the enhanced production of added-value products. However, it requires retrieval and extraction of relevant information from literature, leading to high costs regarding manual curation. Text mining tools implementing methods addressing tasks as information retrieval, named entity recognition and event extraction have been developed to automate and reduce the time required to obtain relevant information from literature in many biomedical fields. However, current tools are not designed or optimized for the purpose of identifying mentions to essential genes in scientific texts.
In this work, we propose a pipeline to automatically extract mentions to genes and to classify them accordingly to their essentiality for a specific organism. This pipeline implements a machine learning approach that is trained using a manually curated set of documents related with gene essentiality in yeast. This corpus is provided as a resource for the community, as a benchmark for the development of new methods. Our pipeline was evaluated performing resampling and cross validation over this curated dataset, presenting an accuracy of over 80%, and an f1-score over 75%.
This work is co-funded by the North Portugal Regional Operational Programme, under the “Portugal 2020”, through the European Regional Development Fund (ERDF), within project SISBI- Refa NORTE-01-0247-FEDER-003381.
The Centre of Biological Engineering (CEB), University of Minho, sponsored all computational hardware and software required for this work.
Conflict of Interest
The authors declare they have no conflict of interests regarding this article.
- 4.Cherry, J.M., Hong, E.L., Amundsen, C., Balakrishnan, R., Binkley, G., Chan, E.T., Christie, K.R., Costanzo, M.C., Dwight, S.S., Engel, S.R., Fisk, D.G., Hirschman, J.E., Hitz, B.C., Karra, K., Krieger, C.J., Miyasato, S.R., Nash, R.S., Park, J., Skrzypek, M.S., Simison, M., Weng, S., Wong, E.D.: Saccharomyces genome database: the genomics resource of budding yeast. Nucleic Acids Res. 40(Database issue), D700-D705 (2012)Google Scholar
- 5.Shatkay, H., Craven, M.: Mining the Biomedical Literature. Computational Molecular Biology. MIT Press, Cambridge (2012)Google Scholar
- 7.Gooch, P.: BADREX: In situ expansion and coreference of biomedical abbreviations using dynamic regular expressions. CoRR abs/1206.4, p. 6 (2012)Google Scholar
- 10.Yakushiji, A., Tateisi, Y., Miyao, Y., Tsujii, J.: Event extraction from biomedical papers using a full parser. In: Pacific Symposium on Biocomputing, pp. 408–419 (2001)Google Scholar
- 11.McClosky, D., Surdeanu, M., Manning, C.D.: Event extraction as dependency parsing. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1, HLT 2011, Stroudsburg, PA, USA, pp. 1626–1635. Association for Computational Linguistics (2011)Google Scholar
- 12.Chun, H., Hwang, Y., Rim, H.-C.: Unsupervised event extraction from biomedical literature using co-occurrence information and basic patterns. In: Su, K.-Y., Tsujii, J., Lee, J.-H., Kwong, O.Y. (eds.) IJCNLP 2004. LNCS (LNAI), vol. 3248, pp. 777–786. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-30211-7_83CrossRefGoogle Scholar
- 13.McCallum, A.K.: MALLET: a machine learning for language toolkit (2002). http://mallet.cs.umass.edu
- 15.Rodrigues, R., Costa, H., Rocha, M.: Development of a machine learning framework for biomedical text mining. In: Saberi Mohamad, M., Rocha, M., Fdez-Riverola, F., Domínguez Mayo, F., De Paz, J. (eds.) PACBB 2016. AISC, vol. 477, pp. 41–49. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40126-3_5CrossRefGoogle Scholar