Literature DB >> 16414044

BIOREL: the benchmark resource to estimate the relevance of the gene networks.

Alexey V Antonov1, Hans W Mewes.   

Abstract

The progress of high-throughput methodologies in functional genomics has lead to the development of statistical procedures to infer gene networks from various types of high-throughput data. However, due to the lack of common standards, the biological significance of the results of the different studies is hard to compare. To overcome this problem we propose a benchmark procedure and have developed a web resource (BIOREL), which is useful for estimating the biological relevance of any genetic network by integrating different sources of biological information. The associations of each gene from the network are classified as biologically relevant or not. The proportion of genes in the network classified as "relevant" is used as the overall network relevance score. Employing synthetic data we demonstrated that such a score ranks the networks fairly in respect to the relevance level. Using BIOREL as the benchmark resource we compared the quality of experimental and theoretically predicted protein interaction data.

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Year:  2006        PMID: 16414044     DOI: 10.1016/j.febslet.2005.12.101

Source DB:  PubMed          Journal:  FEBS Lett        ISSN: 0014-5793            Impact factor:   4.124


  3 in total

1.  R spider: a network-based analysis of gene lists by combining signaling and metabolic pathways from Reactome and KEGG databases.

Authors:  Alexey V Antonov; Esther E Schmidt; Sabine Dietmann; Maria Krestyaninova; Henning Hermjakob
Journal:  Nucleic Acids Res       Date:  2010-06-02       Impact factor: 16.971

2.  A Protein Classification Benchmark collection for machine learning.

Authors:  Paolo Sonego; Mircea Pacurar; Somdutta Dhir; Attila Kertész-Farkas; András Kocsor; Zoltán Gáspári; Jack A M Leunissen; Sándor Pongor
Journal:  Nucleic Acids Res       Date:  2006-11-16       Impact factor: 16.971

3.  ProfCom: a web tool for profiling the complex functionality of gene groups identified from high-throughput data.

Authors:  Alexey V Antonov; Thorsten Schmidt; Yu Wang; Hans W Mewes
Journal:  Nucleic Acids Res       Date:  2008-05-06       Impact factor: 16.971

  3 in total

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