Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/36372

TitleA text mining approach for the extraction of kinetic information from literature
Author(s)Freitas, Ana A.
Costa, Hugo
Rocha, Miguel
Rocha, I.
KeywordsEnzyme kinetics
Metabolic models
Text mining
Name entity recognition
Relation extraction
Databases
Issue date2015
PublisherSpringer
JournalAdvances in Intelligent Systems and Computing
CitationFreitas, A.; Costa, H.; Rocha, Miguel; Rocha, I., A text mining approach for the extraction of kinetic information from literature. Advances in Intelligent Systems and Computing, 375, 89-98, 2015
Abstract(s)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.
TypeConference paper
URIhttp://hdl.handle.net/1822/36372
ISBN9783319197753
DOI10.1007/978-3-319-19776-0_10
ISSN2194-5357
e-ISSN2194-5365
Publisher versionhttp://www.springer.com/series/11156
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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