title: Learning delayed influences of biological systems.
abstract: Boolean networks are widely used model to represent gene interactions and global dynamical behavior of gene regulatory networks. To understand the memory effect involved in some interactions between biological components, it is necessary to include delayed influences in the model. In this paper, we present a logical method to learn such models from sequences of gene expression data. This method analyzes each sequence one by one to iteratively construct a Boolean network that captures the dynamics of these observations. To illustrate the merits of this approach, we apply it to learning real data from bioinformatic literature. Using data from the yeast cell cycle, we give experimental results and show the scalability of the method. We show empirically that using this method we can handle millions of observations and successfully capture delayed influences of Boolean networks.
authors: The Graduate University for Advanced Studies (Sokendai) , Tokyo , Japan.
authors: Tony
authors: T
authors: Ribeiro
authors: National Institute of Informatics , Tokyo , Japan ; Institut de Recherche en Communications et Cybernétique de Nantes (IRCCyN) , Nantes , France.
authors: Morgan
authors: M
authors: Magnin
authors: The Graduate University for Advanced Studies (Sokendai) , Tokyo , Japan ; National Institute of Informatics , Tokyo , Japan.
authors: Katsumi
authors: K
authors: Inoue
authors: Department of Computer and Communication Sciences, Wakayama University , Wakayama , Japan.
authors: Chiaki
authors: C
authors: Sakama
keywords: Boolean network; delayed influences; gene regulatory networks; logic programming; machine learning; state transitions; time delay
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