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Genomics. 2018 Oct 10. pii: S0888-7543(18)30470-1. doi: 10.1016/j.ygeno.2018.10.003. [Epub ahead of print]

Performance evaluation measures for protein complex prediction.

Author information

1
Database Research Group (DBRG), Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
2
Bioinformatics and Computational Omics. Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University (TMU), Tehran, Iran. Electronic address: zahiri@modares.ac.ir.
3
Database Research Group (DBRG), Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. Electronic address: Rahgozar@ut.ac.ir.
4
Institute for Molecular Bioscience, the University of Queensland, St Lucia, Queensland 4072, Australia.

Abstract

Protein complexes play a dominant role in cellular organization and function. Prediction of protein complexes from the network of physical interactions between proteins (PPI networks) has thus become one of the important research areas. Recently, many computational approaches have been developed to identify these complexes. Various performance assessment measures have been proposed for evaluating the efficiency of these methods. However, there are many inconsistencies in the definitions and usage of the measures across the literature. To address this issue, we have gathered and presented the most important performance evaluation measures and developed a tool, named CompEvaluator, to critically assess the protein complex prediction methods. The tool and documentation are publicly available at https://sourceforge.net/projects/compevaluator/files/.

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