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Finding kinetic parameters using text mining

MPS-Authors

Schmeier,  Sebastian
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50393

Kowald,  Axel
Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50384

Klipp,  Edda
Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Citation

Hakenberg, J., Schmeier, S., Kowald, A., Klipp, E., & Leser, U. (2004). Finding kinetic parameters using text mining. OMICS: A Journal of Integrative Biology, 8(2), 131-152.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-881C-4
Abstract
The mathematical modeling and description of complex biological processes has become more and more important over the last years. Systems biology aims at the computational simulation of complex systems, up to whole cell simulations. An essential part focuses on solving a large number of parameterized differential equations. However, measuring those parameters is an expensive task, and finding them in the literature is very laborious. We developed a text mining system that supports researchers in their search for experimentally obtained parameters for kinetic models. Our system classifies full text documents regarding the question whether or not they contain appropriate data using a support vector machine. We evaluated our approach on a manually tagged corpus of 800 documents and found that it outperforms keyword searches in abstracts by a factor of five in terms of precision.