Cytoscape

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 82 articles )

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  1. Bima, Abdulhadi Ibrahim H.; Elsamanoudy, Ayman Zaky; Albaqami, Walaa F.; Khan, Zeenath; Parambath, Snijesh Valiya; Al-Rayes, Nuha; Kaipa, Prabhakar Rao; Elango, Ramu; Banaganapalli, Babajan; Shaik, Noor A.: Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes (2022)
  2. Huang, Rui; Liao, Xiwen; Li, Qiaochuan: Integrative genomic analysis of a novel small nucleolar RNAs prognostic signature in patients with acute myelocytic leukemia (2022)
  3. Liu, Yi; Cheng, Long; Song, Xiangyang; Li, Chao; Zhang, Jiantao; Wang, Lei: A TP53-associated immune prognostic signature for the prediction of the overall survival and therapeutic responses in pancreatic cancer (2022)
  4. Alberto Garcia-Robledo, Mahboobeh Zangiabady: Dash Sylvereye: A WebGL-powered Library for Dashboard-driven Visualization of Large Street Networks (2021) arXiv
  5. Mahesh, K. B.; Rajendra, R.; Reddy, P. Siva Kota: Square root stress-sum index for graphs (2021)
  6. Guo, Q.; Sowa, A.: An almost-solvable model of complex network dynamics (2020)
  7. Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
  8. Michail, Dimitrios; Kinable, Joris; Naveh, Barak; Sichi, John V.: JGraphT -- a Java library for graph data structures and algorithms (2020)
  9. Nahálková, Jarmila: The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins (2020)
  10. Payandeh, Zahra; Rahbar, Mohammad Reza; Jahangiri, Abolfazl; Hashemi, Zahra Sadat; Zakeri, Alireza; Jafarisani, Moslem; Rasaee, Mohammad Javad; Khalili, Saeed: Design of an engineered ACE2 as a novel therapeutics against COVID-19 (2020)
  11. Sanchez, Martin Jose Angel; Petre, Ion: Network controllability analysis of three multiple-myeloma patient genetic mutation datasets (2020)
  12. Zanin, Massimiliano; Papo, David: Assessing functional propagation patterns in COVID-19 (2020)
  13. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  14. K. Hunter Wapman; Daniel B. Larremore: webweb: a tool for creating, displaying, and sharinginteractive network visualizations on the web (2019) not zbMATH
  15. Liang, Yulan; Kelemen, Adam; Kelemen, Arpad: Reproducibility of biomarker identifications from mass spectrometry proteomic data in cancer studies (2019)
  16. Li, Gaoshi; Li, Min; Peng, Wei; Li, Yaohang; Pan, Yi; Wang, Jianxin: A novel extended Pareto optimality consensus model for predicting essential proteins (2019)
  17. Lindsay Rutter, Susan VanderPlas, Dianne Cook, Michelle A. Graham: ggenealogy: An R Package for Visualizing Genealogical Data (2019) not zbMATH
  18. Xiang, Ju; Zhang, Yan; Li, Jian-Ming; Li, Hui-Jia; Li, Min: Identifying multi-scale communities in networks by asymptotic surprise (2019)
  19. Lum, Oliver; Golden, Bruce; Wasil, Edward: An open-source desktop application for generating arc-routing benchmark instances (2018)
  20. Pardy, Christopher; Galbraith, Sally; Wilson, Susan R.: Integrative exploration of large high-dimensional datasets (2018)

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