title: C-DEVA: Detection, evaluation, visualization and annotation of clusters from biological networks.
abstract: With the progress of studies and researches on the biological networks, plenty of excellent clustering algorithms have been proposed. Nevertheless, not only different algorithms but also the same algorithms with different characteristics result in different performances on the same biological networks. Therefore, it might be difficult for researchers to choose an appropriate clustering algorithm to use for a specific network. Here we present C-DEVA, a comprehensive platform for Detecting clusters from biological networks and its Evaluation, Visualization and Annotation analysis. Ten clustering methods are provided in C-DEVA, covering different types of clustering algorithms, with a discrepancy in principle of each type. For the identified complexes, there are over ten popular and traditional bio-statistical measurements to assess them. And multi-source biological information has been integrated in C-DEVA, such as biology-functional annotations, and gold standard complex sets, which are collected from latest datasets in major databases or related papers. Furthermore, visualization analyses are available throughout the whole workflow, which endows C-DEVA with good usability and simple manipulation. To assure extensibility, development interfaces are offered in C-DEVA, for integrating new clustering as well as evaluating methods. Additionally, operations to the network as for example network randomization are also supported. C-DEVA provides a complete tool for identifying clusters from biological networks. Multiple options are offered during the analysis process, including detection methods, evaluation metrics and visualization modules. In addition, researchers could customize C-DEVA for the workflow according to the properties of their networks, and find the most ideal results. C-DEVA is released under the GNU General Public License (GPL), and the source code and binaries are freely available at https://github.com/cici333/c-deva.
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, 410083, China. Electronic address: limin@mail.csu.edu.cn.
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, 410083, China. Electronic address: tang_yu@csu.edu.cn.
authors.forename: Yu
authors.initials: Y
authors.lastname: Tang
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, 410083, China. Electronic address: csu.xhwu@csu.edu.cn.
authors.forename: Xuehong
authors.initials: X
authors.lastname: Wu
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, 410083, China. Electronic address: jxwang@mail.csu.edu.cn.
authors.forename: Jianxin
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, 410083, China; Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, S7N 5A9, Canada. Electronic address: faw341@mail.usask.ca.
authors.forename: Fang-Xiang
authors.initials: FX
authors.lastname: Wu
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, 410083, China; Department of Computer Science, Georgia State University, Atlanta, GA, 30302-4110, USA. Electronic address: pan@cs.gsu.edu.
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D058977:Molecular Sequence Annotation / Q000379:methods*
keywords: Clustering; Evaluation; Visualization and annotation