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. 2006 Jan 10;34(1):e6.
doi: 10.1093/nar/gnj002.

A systematic approach to infer biological relevance and biases of gene network structures

Alexey V Antonov  1 , Igor V Tetko, Hans W Mewes
Affiliations

A systematic approach to infer biological relevance and biases of gene network structures

Alexey V Antonov et al. Nucleic Acids Res. .

Abstract

The development of high-throughput technologies has generated the need for bioinformatics approaches to assess the biological relevance of gene networks. Although several tools have been proposed for analysing the enrichment of functional categories in a set of genes, none of them is suitable for evaluating the biological relevance of the gene network. We propose a procedure and develop a web-based resource (BIOREL) to estimate the functional bias (biological relevance) of any given genetic network by integrating different sources of biological information. The weights of the edges in the network may be either binary or continuous. These essential features make our web tool unique among many similar services. BIOREL provides standardized estimations of the network biases extracted from independent data. By the analyses of real data we demonstrate that the potential application of BIOREL ranges from various benchmarking purposes to systematic analysis of the network biology.

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Figures

Figure 1
Figure 1
Statistical model used to generate synthetic data. The edge weight is increasing along horizontal axis and the probability (density) to generate such weight for relevant (left curve on each plot)/irrelevant (right curve on each plot) association in the synthetic network is shown.
Figure 2
Figure 2
The bias score (computed by BIOREL) in the synthetic data is plotted against parameter α. The bias score is a proportion of genes in the network with significantly (P < 0.01) biased associations.
See this image and copyright information in PMC

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