title: A systematic approach to infer biological relevance and biases of gene network structures.
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.
authors.affiliation: GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany. antonov@gsf.de
authors.forename: Alexey V
authors.initials: AV
authors.lastname: Antonov
authors.forename: Igor V
authors.initials: IV
authors.lastname: Tetko
authors.forename: Hans W
authors.initials: HW
authors.lastname: Mewes
mesh_terms: D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005796:Genes; D023281:Genomics / Q000379:methods*; D020407:Internet; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software; D020798:Two-Hybrid System Techniques