Abstract
Systems biology has fostered interest in the use of kinetic models to better understand the dynamic behavior of metabolic networks in a wide variety of conditions. Unfortunately, in most cases, data available in different databases are not sufficient for the development of such models, since a significant part of the relevant information is still scattered in the literature. Thus, it becomes essential to develop specific and powerful text mining tools towards this aim. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The pipeline proposed integrates the development of a novel plug-in over the text mining tool @Note2. Overall, the results validate the developed approach.
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Acknowledgments
The work was funded by National Funds through the FCT (Portuguese Foundation for Science and Technology) within project ref. PTDC/QUI-BIQ/119657/2010 Finding the naturally evolved design principles of prevalent metabolic circuits. The authors would also like to thank the FCT Strategic Project PEst-OE/EQB/ LA0023/2013 and the Projects BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes, REF. NORTE-07-0124-FEDER-000028 and PEM Metabolic Engineering Platform, project number 23060, both co-funded by the Programa Operacional Regional do Norte (ON.2 O Novo Norte), QREN, FEDER.
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Freitas, A.A., Costa, H., Rocha, M., Rocha, I. (2015). A Text Mining Approach for the Extraction of Kinetic Information from Literature. In: Overbeek, R., Rocha, M., Fdez-Riverola, F., De Paz, J. (eds) 9th International Conference on Practical Applications of Computational Biology and Bioinformatics. Advances in Intelligent Systems and Computing, vol 375. Springer, Cham. https://doi.org/10.1007/978-3-319-19776-0_10
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DOI: https://doi.org/10.1007/978-3-319-19776-0_10
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