In this paper, the regulatory interactions between genes are modeled by a linear genetic
network that is estimated from gene expression data. The inference of such a genetic …

With the completion of the sequencing of the human genome, the need for tools capable of
unraveling the interaction and functionality of genes becomes extremely urgent. In answer to …

Currently, the need arises for tools capable of unraveling the functionality of genes based on
the analysis of microarray measurements. Modeling genetic interactions by means of …

Networks are often used to represent the interactions among genes and proteins. These
interactions are known to play an important role in vital cell functions and should be included …

As molecular biology continues to evolve in the direction of high-throughput collection of
data, it has become increasingly necessary to develop computational methods for analyzing …

We propose a system named AIGNET (Algorithms for Inference of Genetic Networks), and
introduce two top-down approaches for the inference of interrelated mechanism among …

Mathematical models are useful for providing a framework for integrating data and gaining
insights into the static and dynamic behavior of complex biological systems such as …

ABSTRACT A model genetic regulatory network for the evaluation of genetic regulatory
network identification methods is described in the present work. This model is novel …

In this paper we introduce a new inference method of a gene regulatory network from
steadystate gene expression data. Our method determines a regulatory structure consistent …

Motivation: There has been considerable interest in developing computational techniques
for inferring genetic regulatory networks from whole-genome expression profiles. When …