title: A gene network inference method from continuous-value gene expression data of wild-type and mutants.
abstract: In this paper we introduce a new inference method of a gene regulatory network from steady-state gene expression data. Our method determines a regulatory structure consistent with an observed set of steady-state expression profiles, each generated from wild-type and single deletion mutant of the target network. Our method derives the regulatory relationships in the network using a graph theoretic approach. The advantage of our method is to be able to deal with continuous values of steady-state data, while most of the methods proposed in past use a Boolean network model with binary data. Performance of our method is evaluated on simulated networks with varying the size of networks, indegree of each gene, and the data characteristics (continuous-value/binary), and is compared with that of predictor method proposed by Ideker et al. As a result, we show the superiority of using continuous values to binary values, and the performance of our method is much better than that of the predictor method.
authors.affiliation: Kitano Symbiotic Systems Project, ERATO, JST., M31 6A, 6-31-15 Jingumae, Shibuya-ku, Tokyo 150-0001, Japan. kyoda@symbio.jst.go.jp
authors.forename: K M
authors.initials: KM
authors.lastname: Kyoda
authors.forename: M
authors.initials: M
authors.lastname: Morohashi
authors.forename: S
authors.initials: S
authors.lastname: Onami
authors.forename: H
authors.initials: H
authors.lastname: Kitano
mesh_terms: D000818:Animals; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D008957:Models, Genetic; D009154:Mutation; D015003:Yeasts / Q000235:genetics