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 73 articles )

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  1. Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
  2. Michail, Dimitrios; Kinable, Joris; Naveh, Barak; Sichi, John V.: JGraphT -- a Java library for graph data structures and algorithms (2020)
  3. Nahálková, Jarmila: The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins (2020)
  4. 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)
  5. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  6. K. Hunter Wapman; Daniel B. Larremore: webweb: a tool for creating, displaying, and sharinginteractive network visualizations on the web (2019) not zbMATH
  7. Liang, Yulan; Kelemen, Adam; Kelemen, Arpad: Reproducibility of biomarker identifications from mass spectrometry proteomic data in cancer studies (2019)
  8. Li, Gaoshi; Li, Min; Peng, Wei; Li, Yaohang; Pan, Yi; Wang, Jianxin: A novel extended Pareto optimality consensus model for predicting essential proteins (2019)
  9. Lindsay Rutter, Susan VanderPlas, Dianne Cook, Michelle A. Graham: ggenealogy: An R Package for Visualizing Genealogical Data (2019) not zbMATH
  10. Lum, Oliver; Golden, Bruce; Wasil, Edward: An open-source desktop application for generating arc-routing benchmark instances (2018)
  11. Pardy, Christopher; Galbraith, Sally; Wilson, Susan R.: Integrative exploration of large high-dimensional datasets (2018)
  12. Song, Wei; Liu, Huaping; Wang, Jiajia; Kong, Yan; Yin, Xia; Zang, Weidong: MATHT: a web server for comprehensive transcriptome data analysis (2018)
  13. Teng, Yanbo; Ding, Yanjun; Zhang, Mingming; Chen, Xinren; Wang, Xizi; Yu, Hang; Liu, Chonghui; Lv, Hongchao; Zhang, Ruijie: Genome-wide haplotype association study identifies risk genes for non-small cell lung cancer (2018)
  14. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  15. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  16. De Niz, Carlos; Rahman, Raziur; Zhao, Xiangyuan; Pal, Ranadip: Algorithms for drug sensitivity prediction (2016)
  17. Fire, Michael; Puzis, Rami: Organization mining using online social networks (2016)
  18. Indhumathy, M.; Arumugam, S.; Baths, Veeky; Singh, Tarkeshwar: Graph theoretic concepts in the study of biological networks (2016)
  19. Liang, Yulan; Kelemen, Arpad: Bayesian state space models for dynamic genetic network construction across multiple tissues (2016)
  20. Alfonso Niño, Camelia Muñoz-Caro, Sebastián Reyes: APINetworks: A general API for the treatment of complex networks in arbitrary computational environments (2015) not zbMATH

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