Identifying genes of gene regulatory networks using formal concept analysis
Gebert, Jutta and Motameny, Susanne and Faigle, Ulrich and Forst, Christian V. and Schrader, Rainer
Identifying genes of gene regulatory networks using formal concept analysis.
Published in: Journal of Computational Biology Vol. 15 (2). pp. 185-194.
In order to understand the behavior of a gene regulatory network, it is essential to know the genes that belong to it. Identifying the correct members (e.g. in order to build a model) is a difficult task even for small subnetworks. Usually only few members of a network are known and one needs to guess the missing members based on experience or informed speculation. It is beneficial if one can additionally rely on experimental data to support this guess. In this work we present a new method based on formal concept analysis to detect unknown members of a gene regulatory network from gene expression time series data. We show that formal concept analysis is able to find a list of candidate genes for inclusion into a partially known basic network. This list can then be reduced by a statistical analysis so that the resulting genes interact strongly with the basic network and therefore should be included when modeling the network. The method has been applied to the DNA repair system of Mycobacterium tuberculosis. In this application our method produces comparable results to an already existing method of component selection while it is applicable to a broader range of problems.
|Citations:||8 (Google Scholar) | 2 (Web of Science)|
|Uncontrolled Keywords:||gene regulatory networks formal concept analysis component selection|
|Divisions:||Institute of Computer Science > Computer Science Department - Prof. Dr. Schrader
Mathematical Institute > Prof. Dr. Faigle
|Depositing User:||Susanne Motameny|
|Date Deposited:||27 Mar 2008 00:00|
|Last Modified:||09 Jan 2012 12:42|