Journal article

Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets

Piyush B Madhamshettiwar, Stefan R Maetschke, Melissa J Davis, Antonio Reverter, Mark A Ragan

Genome Medicine: medicine in the post-genomic era | BMC | Published : 2012

Abstract

BACKGROUND: Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. METHODS: We report a comprehensi..

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University of Melbourne Researchers

Grants

Awarded by Queensland Facility for Advanced Bioinformatics through Australian Research Council


Awarded by Australian Research Council


Funding Acknowledgements

Computational resources were provided by National Computational Infrastructure Specialised Facility in Bioinformatics. Access to GeneGo, IPA and TRANSFAC was provided by Queensland Facility for Advanced Bioinformatics through Australian Research Council grant LE098933. PBM, SRM, MJD and MAR acknowledge support of Australian Research Council grants CE0348221, DP110103384 and LE0989334.