Abstract
BackgroundReverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods.ResultsWe propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step.ConclusionsOur algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network sub-challenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference.
Highlights
Reverse engineering is an interesting area of research currently receiving a lot of attentions from the Systems Biology community
The outline of the paper is the following: we first describe the DREAM4 In Silico Network challenges, explain the inference algorithm we devised and applied to the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 4 data, followed by a description of the gene network (GN) simulator we developed to generate additional synthetic networks and data
We present the results of re-analysis of the DREAM4 In Silico benchmarks, which we were able to perform after the gold standard networks were released
Summary
Reverse engineering is an interesting area of research currently receiving a lot of attentions from the Systems Biology community. The causal influence of gene A on gene B could be due to the transcription activation of gene B by the protein product of gene A upon binding to gene B’s promoter sequence (as in a transcription factor–target relationship), and be due to more complicated processes, such as gene A encoding a metabolic enzyme producing a metabolite which in turn regulates the transcription of gene B. These detailed biochemical events are hidden to the observed set of variables (gene expression levels) and their effects will merely result in an observable causal effect A?B. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods
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