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Structural influence of gene networks on their inference: analysis of C3NET

Gökmen Altay12 and Frank Emmert-Streib12*

Author Affiliations

1 Computational Biology and Machine Learning Lab, Center for Cancer Research and Cell Biology, School of Medicine, Dentistry and Biomedical Sciences, Queen's University Belfast, 97 Lisburn Road, Belfast, BT9 7BL, UK

2 Cambridge Research Institute, Department of Oncology, University of Cambridge, CB2 0RE, Cambridge, UK

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Biology Direct 2011, 6:31  doi:10.1186/1745-6150-6-31

Published: 22 June 2011

Abstract

Background

The availability of large-scale high-throughput data possesses considerable challenges toward their functional analysis. For this reason gene network inference methods gained considerable interest. However, our current knowledge, especially about the influence of the structure of a gene network on its inference, is limited.

Results

In this paper we present a comprehensive investigation of the structural influence of gene networks on the inferential characteristics of C3NET - a recently introduced gene network inference algorithm. We employ local as well as global performance metrics in combination with an ensemble approach. The results from our numerical study for various biological and synthetic network structures and simulation conditions, also comparing C3NET with other inference algorithms, lead a multitude of theoretical and practical insights into the working behavior of C3NET. In addition, in order to facilitate the practical usage of C3NET we provide an user-friendly R package, called c3net, and describe its functionality. It is available from https://r-forge.r-project.org/projects/c3net webcite and from the CRAN package repository.

Conclusions

The availability of gene network inference algorithms with known inferential properties opens a new era of large-scale screening experiments that could be equally beneficial for basic biological and biomedical research with auspicious prospects. The availability of our easy to use software package c3net may contribute to the popularization of such methods.

Reviewers

This article was reviewed by Lev Klebanov, Joel Bader and Yuriy Gusev.