A systematic approach to infer biological relevance and biases of gene network structures
Author(s) -
Alexey V. Antonov
Publication year - 2006
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gnj002
Subject(s) - relevance (law) , biology , benchmarking , biological network , gene regulatory network , computational biology , biological data , network analysis , set (abstract data type) , computer science , data mining , data science , gene , bioinformatics , genetics , gene expression , physics , marketing , quantum mechanics , political science , law , business , programming language
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 applica- tion of BIOREL ranges from various benchmarking purposes to systematic analysis of the network biology.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom