![cytoscape networks cytoscape networks](https://i.ytimg.com/vi/ZwaTTCcA-fo/maxresdefault.jpg)
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#CYTOSCAPE NETWORKS SOFTWARE#
Klukas C, Junker BH, Schreiber F (2006) The VANTED software system for transcriptomics, proteomics and metabolomics analysis. R Foundation for Statistical Computing, Vienna, Austriaīare JC, Shannon PT, Schmid AK, Baliga NS (2007) The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications. Team RDC (2005) R: a language and environment for statistical computing. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N et al (2003) TM4: a free, open-source system for microarray data management and analysis. Shannon PT, Reiss DJ, Bonneau R, Baliga NS (2006) The Gaggle: an open-source software system for integrating bioinformatics software and data sources. Krummenacker M, Paley S, Mueller L, Yan T, Karp PD (2005) Querying and computing with BioCyc databases. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J et al (2009) STRING 8: a global view on proteins and their functional interactions in 630 organisms. Von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M et al (2005) STRING: known and predicted protein–protein associations, integrated and transferred across organisms. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: kyoto encyclopedia of genes and genomes. Rho S, You S, Kim Y, Hwang D (2008) From proteomics toward systems biology: integration of different types of proteomics data into network models. Bioinformatics 21:3448–3449Ĭline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C et al (2007) Integration of biological networks and gene expression data using cytoscape. Maere S, Heymans K, Kuiper M (2005) BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21:430–438īader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. Vailaya A, Bluvas P, Kincaid R, Kuchinsky A, Creech M, Adler A (2005) An architecture for biological information extraction and representation. Nucleic Acids Res 32:D258–D261Īshburner M, Ball CA, Blake JA, Butler H, Cherry JM, Corradi J et al (2001) Creating the gene ontology resource: design and implementation. Harris MA, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R et al (2004) The gene ontology (GO) database and informatics resource. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. Furthermore, special features of alternative software tools are highlighted in order to assist researchers in the choice of an adequate program for their specific requirements. Additional information about the use of Cytoscape is provided in the Notes section.
![cytoscape networks cytoscape networks](https://www.cgl.ucsf.edu/cytoscape/cddApp/Figure1.png)
Utilization of visualization software is exemplified using the widely used Cytoscape tool. Global requirements for such programs are discussed. This chapter focuses on software tools that assist in visual exploration and analysis of biological networks. Obviously, there is a high demand for computer-based assistance for both visualization and analysis of biological data, which are often heterogeneous and retrieved from different sources. Nodes are representations of biological molecules and edges connect the nodes depicting some kind of relationship. In order to represent large biological data sets in an easily interpretable manner, this information is frequently visualized as graphs, i.e., a set of nodes and edges. Substantial progress has been made in the field of “omics” research (e.g., Genomics, Transcriptomics, Proteomics, and Metabolomics), leading to a vast amount of biological data.