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Open Access Research article

CASCADE_SCAN: mining signal transduction network from high-throughput data based on steepest descent method

Kai Wang1, Fuyan Hu2, Kejia Xu2, Hua Cheng1, Meng Jiang1, Ruili Feng1, Jing Li1 and Tieqiao Wen1*

Author Affiliations

1 Laboratory of Molecular Neurobiology, School of Life Sciences and Institute of Systems Biology, Shanghai University, Shanghai 200444, China

2 Department of Mathematics, College of Sciences, Shanghai University, Shanghai 200444, China

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BMC Bioinformatics 2011, 12:164  doi:10.1186/1471-2105-12-164

Published: 17 May 2011

Abstract

Background

Signal transduction is an essential biological process involved in cell response to environment changes, by which extracellular signaling initiates intracellular signaling. Many computational methods have been generated in mining signal transduction networks with the increasing of high-throughput genomic and proteomic data. However, more effective means are still needed to understand the complex mechanisms of signaling pathways.

Results

We propose a new approach, namely CASCADE_SCAN, for mining signal transduction networks from high-throughput data based on the steepest descent method using indirect protein-protein interactions (PPIs). This method is useful for actual biological application since the given proteins utilized are no longer confined to membrane receptors or transcription factors as in existing methods. The precision and recall values of CASCADE_SCAN are comparable with those of other existing methods. Moreover, functional enrichment analysis of the network components supported the reliability of the results.

Conclusions

CASCADE_SCAN is a more suitable method than existing methods for detecting underlying signaling pathways where the membrane receptors or transcription factors are unknown, providing significant insight into the mechanism of cellular signaling in growth, development and cancer. A new tool based on this method is freely available at http://www.genomescience.com.cn/CASCADE_SCAN/ webcite.