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

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 ev...

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Detalles Bibliográficos
Autores principales: Antonov, Alexey V., Tetko, Igor V., Mewes, Hans W.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1326251/
https://www.ncbi.nlm.nih.gov/pubmed/16407322
http://dx.doi.org/10.1093/nar/gnj002
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author Antonov, Alexey V.
Tetko, Igor V.
Mewes, Hans W.
author_facet Antonov, Alexey V.
Tetko, Igor V.
Mewes, Hans W.
author_sort Antonov, Alexey V.
collection PubMed
description 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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spelling pubmed-13262512006-01-17 A systematic approach to infer biological relevance and biases of gene network structures Antonov, Alexey V. Tetko, Igor V. Mewes, Hans W. Nucleic Acids Res Methods Online 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. Oxford University Press 2006 2006-01-10 /pmc/articles/PMC1326251/ /pubmed/16407322 http://dx.doi.org/10.1093/nar/gnj002 Text en © The Author 2006. Published by Oxford University Press. All rights reserved
spellingShingle Methods Online
Antonov, Alexey V.
Tetko, Igor V.
Mewes, Hans W.
A systematic approach to infer biological relevance and biases of gene network structures
title A systematic approach to infer biological relevance and biases of gene network structures
title_full A systematic approach to infer biological relevance and biases of gene network structures
title_fullStr A systematic approach to infer biological relevance and biases of gene network structures
title_full_unstemmed A systematic approach to infer biological relevance and biases of gene network structures
title_short A systematic approach to infer biological relevance and biases of gene network structures
title_sort systematic approach to infer biological relevance and biases of gene network structures
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1326251/
https://www.ncbi.nlm.nih.gov/pubmed/16407322
http://dx.doi.org/10.1093/nar/gnj002
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