ENNET: inferring large gene regulatory networks from expression data using gradient boosting

BMC Systems Biology

Tomasz Arodź¹  Janusz Sławek¹ 

[1] Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia

关键词: Boosting;    Ensemble learning;    Network inference;    Gene regulatory networks;   

Others  :  1142063
DOI  :  10.1186/1752-0509-7-106

 received in 2013-06-24, accepted in 2013-10-17,  发布年份 2013

【 摘 要 】

Background

The regulation of gene expression by transcription factors is a key determinant of cellular phenotypes. Deciphering genome-wide networks that capture which transcription factors regulate which genes is one of the major efforts towards understanding and accurate modeling of living systems. However, reverse-engineering the network from gene expression profiles remains a challenge, because the data are noisy, high dimensional and sparse, and the regulation is often obscured by indirect connections.

Results

We introduce a gene regulatory network inference algorithm ENNET, which reverse-engineers networks of transcriptional regulation from a variety of expression profiles with a superior accuracy compared to the state-of-the-art methods. The proposed method relies on the boosting of regression stumps combined with a relative variable importance measure for the initial scoring of transcription factors with respect to each gene. Then, we propose a technique for using a distribution of the initial scores and information about knockouts to refine the predictions. We evaluated the proposed method on the DREAM3, DREAM4 and DREAM5 data sets and achieved higher accuracy than the winners of those competitions and other established methods.

Conclusions

Superior accuracy achieved on the three different benchmark data sets shows that ENNET is a top contender in the task of network inference. It is a versatile method that uses information about which gene was knocked-out in which experiment if it is available, but remains the top performer even without such information. ENNET is available for download from https://github.com/slawekj/ennet webcite under the GNU GPLv3 license.

【 授权许可】

   
2013 Sławek and Arodź; licensee BioMed Central Ltd.

【 图 表 】

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