Journal Article

Genetic network inference as a series of discrimination tasks

Shuhei Kimura, Satoshi Nakayama and Mariko Hatakeyama

in Bioinformatics

Volume 25, issue 7, pages 918-925
Published in print April 2009 | ISSN: 1367-4803
Published online February 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp072
Genetic network inference as a series of discrimination tasks

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Motivation: Genetic network inference methods based on sets of differential equations generally require a great deal of time, as the equations must be solved many times. To reduce the computational cost, researchers have proposed other methods for inferring genetic networks by solving sets of differential equations only a few times, or even without solving them at all. When we try to obtain reasonable network models using these methods, however, we must estimate the time derivatives of the gene expression levels with great precision. In this study, we propose a new method to overcome the drawbacks of inference methods based on sets of differential equations.

Results: Our method infers genetic networks by obtaining classifiers capable of predicting the signs of the derivatives of the gene expression levels. For this purpose, we defined a genetic network inference problem as a series of discrimination tasks, then solved the defined series of discrimination tasks with a linear programming machine. Our experimental results demonstrated that the proposed method is capable of correctly inferring genetic networks, and doing so more than 500 times faster than the other inference methods based on sets of differential equations. Next, we applied our method to actual expression data of the bacterial SOS DNA repair system. And finally, we demonstrated that our approach relates to the inference method based on the S-system model. Though our method provides no estimation of the kinetic parameters, it should be useful for researchers interested only in the network structure of a target system.

Contact: kimura@ike.tottori-u.ac.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6099 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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