Literature DB >> 29186337

CytoCtrlAnalyser: a Cytoscape app for biomolecular network controllability analysis.

Lin Wu1, Min Li2, Jianxin Wang2, Fang-Xiang Wu1,3.   

Abstract

Summary: Studying the controllability of biomolecular networks can result in profound knowledge about molecular biological systems. However, there is no comprehensive and easy-to-use platform for analyzing controllability of biomolecular networks although various algorithms for analyzing complex network controllability have been proposed recently. In this application note, we develop the CytoCtrlAnalyser which is a Cytoscape app to provide a comprehensive platform for analyzing controllability of biomolecular networks. Nine algorithms have been integrated in CytoCtrlAnalyser. With network topologies and customized control settings imported into CytoCtrlAnalyser, users can identify the steering nodes which should be actuated by input control signals for achieving different control objectives as well as investigate the importance of nodes from different perspectives in the controllability of networks. CytoCtrlAnalyser offers a tool for many promising applications, such as identification of potential drug targets or biologically important nodes in biomolecular networks. Availability and implementation: Freely available for downloading at http://apps.cytoscape.org/apps/cytoctrlanalyser. Contact: faw341@mail.usask.ca. Supplementary information: Supplementary data are available at Bioinformatics online.

Mesh:

Year:  2018        PMID: 29186337     DOI: 10.1093/bioinformatics/btx764

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  3 in total

1.  Special Protein Molecules Computational Identification.

Authors:  Quan Zou; Wenying He
Journal:  Int J Mol Sci       Date:  2018-02-10       Impact factor: 5.923

2.  Network-based Observability and Controllability Analysis of Dynamical Systems: the NOCAD toolbox.

Authors:  Dániel Leitold; Ágnes Vathy-Fogarassy; János Abonyi
Journal:  F1000Res       Date:  2019-05-09

3.  A Hybrid Clustering Algorithm for Identifying Cell Types from Single-Cell RNA-Seq Data.

Authors:  Xiaoshu Zhu; Hong-Dong Li; Yunpei Xu; Lilu Guo; Fang-Xiang Wu; Guihua Duan; Jianxin Wang
Journal:  Genes (Basel)       Date:  2019-01-29       Impact factor: 4.096

  3 in total

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