China 
Africa 
Explore chapters and articles related to this topic
Multi-Level Data Analysis in Cancer
Published in Inna Kuperstein, Emmanuel Barillot, Computational Systems Biology Approaches in Cancer Research, 2019
Inna Kuperstein, Emmanuel Barillot
The Cytoscape software platform8,9 offers a wide range of functionality around the visualization and analysis of functional and other networks and network-associated data, with no need for programming. Cytoscape has been developed as an open-source, community-driven software platform since its first release in 2002. Initiated at the Institute for Systems Biology in Seattle by the need to explore some of the first large-scale protein interaction datasets, Cytoscape is now a software platform whose core is maintained and developed by a handful of different systems biology research teams and extends to a worldwide user community. Cytoscape is used across a wide range of applications, but its focus remains on biomedical research, and specifically, on molecular interaction networks.
Clustering Biological Data
Published in Charu C. Aggarwal, Chandan K. Reddy, Data Clustering, 2018
Chandan K. Reddy, Mohammad Al Hasan, Mohammed J. Zaki
clusterMaker [74] provides a unified platform for various traditional clustering and network clustering techniques. It is available at http://www.cgl.ucsf.edu/cytoscape/cluster/clusterMaker.html. All of the network partitioning cluster algorithms create collapsible “meta nodes” to allow interactive exploration. These clustering algorithms have been developed as a plugin to the Cytoscape software [93]. Cytoscape is an open source bioinformatics software platform for visualizing complex molecular interaction networks. It is available at http://www.cytoscape.org/.
Vulnerability hotspot mapping (VHM) of sewer pipes based on deterioration factors
Published in Urban Water Journal, 2023
Afshin Sadeghikhah, Ehtesham Ahmed, Sohini Chakraborty, Stefan Trülzsch, Peter Krebs
Degree of nodes were assessed from Cytoscape which is an open-source software for visualizing complex networks and analysing their node’s connectedness. As included in the Table 4, 44% of the nodes are functioning as the inlet nodes to the system, which are followed by 37% of two-joint pipes, 12% of one-joint pipes, and 7% of three-joint pipes. Finally, vegetation and root intrusion risk were assigned visually based on google maps and criticality classes were implemented in the VHM. The distribution results are presented in Table 4.