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Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)-guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l1-norm regularisation of sparse regression based on a modified version of BIC...
Inferring Gene Regulatory Networks (GRNs) from high-throughput experimental data is an important problem in Systems Biology. In this paper we present a new algorithm for the task. Our algorithm is based on a sparse Bayesian learning framework and works well with steady state gene expression data. To evaluate its performance, we compare our algorithm with two state of the art algorithms on multiple...
Gene regulatory network (GRN) modelling has gained increasing attention in the past decade. Many computational modelling techniques have been proposed to facilitate the inference and analysis of GRN. However, there is often confusion about the aim of GRN modelling, and how a gene network model can be fully utilised as a tool for systems biology. The aim of the present article is to provide an overview...
Inference of gene regulatory network from microarray data is one of the most important issues in bioinformatics. Several algorithms have been introduced for this problem, but they cannot give accurate results in case of time series gene expression data. Here, we propose an algorithm that predicts the gene regulatory network more accurately than the previous methods. A new method finds the relationship...
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