TY - JOUR
AU - Haggarty, Stephen
AB - Background: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results: Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph- generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear 
TI - SpectralNET – an application for spectral graph analysis and visualization
JF - BMC Bioinformatics
DO - 10.1186/1471-2105-6-260
DA - 2005-10-19
UR - https://www.deepdyve.com/lp/springer-journals/spectralnet-an-application-for-spectral-graph-analysis-and-5ChV5FruBH
SP - 1
EP - 13
VL - 6
IS - 1
DP - DeepDyve
ER -