The structural analysis of biological networks includes the ranking of the vertices based on the connection structure of a network. To support this analysis we discuss centrality measures which indicate the importance of vertices, and demonstrate their applicability on a gene regulatory network. We show that common centrality measures result in different valuations of the vertices and that novel measures tailored to specific biological investigations are useful for the analysis of biological networks, in particular gene regulatory networks.publishe
Studying associations among genes and diseases provides an important avenue for a better understand...
In this paper, we present graphics processing unit (GPU) based implementations of three popular shor...
The central vertices in complex networks are of particular interest because they might play the role...
<p>Depending on the number of genes that are considered as top ranking genes, the numbers and combin...
Abstract Background The elucidation of whole-cell regulatory, metabolic, interaction and other biolo...
A strategy for zooming in and out the topological environment of a node in a complex network is deve...
Correlation networks are emerging as powerful tools for modeling relationships in high-throughput da...
Studies in Computational Intelligence book series (SCI, volume 689)Centrality measures are used in n...
In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particu...
Graph centralities are commonly used to identify and prioritize disease genes in transcriptional reg...
MOTIVATION: The interpretation of gene interaction in biological networks generates the need for a m...
Motivation: Modularity analysis is a powerful tool for studying the design of biological networks, o...
Living systems are associated with Social networks — networks made up of nodes, some of which may be...
<p>Association networks capture both physical and regulatory interactions between gene pairs. Networ...
Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-t...
Studying associations among genes and diseases provides an important avenue for a better understand...
In this paper, we present graphics processing unit (GPU) based implementations of three popular shor...
The central vertices in complex networks are of particular interest because they might play the role...
<p>Depending on the number of genes that are considered as top ranking genes, the numbers and combin...
Abstract Background The elucidation of whole-cell regulatory, metabolic, interaction and other biolo...
A strategy for zooming in and out the topological environment of a node in a complex network is deve...
Correlation networks are emerging as powerful tools for modeling relationships in high-throughput da...
Studies in Computational Intelligence book series (SCI, volume 689)Centrality measures are used in n...
In this paper, we present a survey of the use of graph theoretical techniques in Biology. In particu...
Graph centralities are commonly used to identify and prioritize disease genes in transcriptional reg...
MOTIVATION: The interpretation of gene interaction in biological networks generates the need for a m...
Motivation: Modularity analysis is a powerful tool for studying the design of biological networks, o...
Living systems are associated with Social networks — networks made up of nodes, some of which may be...
<p>Association networks capture both physical and regulatory interactions between gene pairs. Networ...
Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-t...
Studying associations among genes and diseases provides an important avenue for a better understand...
In this paper, we present graphics processing unit (GPU) based implementations of three popular shor...
The central vertices in complex networks are of particular interest because they might play the role...