Finding Kinetic Parameters Using Text Mining

Jörg Hakenberg^1*, Sebastian Schmeier², Axel Kowald², Edda Klipp², and Ulf Leser¹

¹ Humboldt-Universität zu Berlin, Dept. Computer Science, Knowledge Management in Bioinformatics, 12489 Berlin, Germany;
² Max-Planck-Institute for Molecular Genetics, Kinetic Modeling Group, 14195 Berlin, Germany;
^* Corresponding author. Current affiliation: Knowledge Management in Bioinformatics, Dept. Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. Phone: +49.30.2093.3903, eMail: hakenberg(a)informatik.hu-berlin.de

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 5 in terms of precision.

Published in
OMICS A Journal of Integrative Biology, Spring 2004, Vol. 8, No. 2, pages 131-152. Special Issue on Data Mining meets Integrative Biology - A Symbiosis in the Making. (to appear)