Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape’s software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

References in zbMATH (referenced in 17 articles )

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  1. Fire, Michael; Puzis, Rami: Organization mining using online social networks (2016)
  2. Liang, Yulan; Kelemen, Arpad: Bayesian state space models for dynamic genetic network construction across multiple tissues (2016)
  3. Curme, Chester; Stanley, H.Eugene; Vodenska, Irena: Coupled network approach to predictability of financial market returns and news sentiments (2015)
  4. Li, Hui; Liu, Chunmei: Biomarker identification using text mining (2012)
  5. Li, Shaoyu; Cui, Yuehua: Gene-centric gene-gene interaction: a model-based kernel machine method (2012)
  6. Chen, Chuming; McGarvey, Peter B.; Huang, Hongzhan; Wu, Cathy H.: Protein bioinformatics infrastructure for the integration and analysis of multiple high-throughput “omics” data (2010) ioport
  7. Likić, Vladimir A.; Mcconville, Malcolm J.; Lithgow, Trevor; Bacic, Antony: Systems biology: the next frontier for bioinformatics (2010)
  8. Nieselt, Kay; Kaufmann, Michael; Gerasch, Andreas; Lenhof, Hans-Peter; Spehr, Marcel; Hesse, Stefan; Gumhold, Stefan: Visuelle Analytik biologischer Daten (2010) ioport
  9. Ren, Xianwen; Zhang, Xiang-Sun: A linear programming model based on network flow for pathway inference (2010)
  10. Roslan, Rosfuzah; Othman, Razib M.; Shah, Zuraini A.; Kasim, Shahreen; Asmuni, Hishammuddin; Taliba, Jumail; Hassan, Rohayanti; Zakaria, Zalmiyah: Incorporating multiple genomic features with the utilization of interacting domain patterns to improve the prediction of protein-protein interactions (2010) ioport
  11. Zanghi, Hugo; Picard, Franck; Miele, Vincent; Ambroise, Christophe: Strategies for online inference of model-based clustering in large and growing networks (2010)
  12. Heath, Allison P.; Kavraki, Lydia E.: Computational challenges in systems biology (2009)
  13. Linde, Jörg; Olsson, Björn; Lubovac, Zelmina: Network properties for ranking predicted miRNa targets in breast cancer (2009) ioport
  14. Schreiber, Falk: Analyse und Visualisierung biologischer Netzwerke (2009) ioport
  15. Wooley, John C.; Ye, Yuzhen: Metagenomics: facts and artifacts, and computational challenges (2009) ioport
  16. Ng, Ka-Lok; Huang, Chien-Hung; Liu, Hsueh-Chuan; Liu, Hsiang-Chuan: Applications of domain-domain interactions in pathway study (2008)
  17. Singhal, Mudita; Domico, Kelly: CABIN: Collective analysis of biological interaction networks (2007)