Corrigendum: Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review
- 1Department of Computer Science, Xiangnan University, Hunan, China
- 2Department of Physics and Biophysics, Institute of Biosciences of Botucatu, São Paulo State University, Botucatu, Brazil
by Zhang, X., Acencio, M. L., and Lemke, N. (2016). Front. Physiol. 7:75. doi: 10.3389/fphys.2016.00075
In the original article, the author Xue Zhang's affiliation is “Department of Computer Science, Xiangnan University, Hunan, China”. Because the author changed her affiliation to “School of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, China” and the publishing fees will be paid by the new affiliation, this has been added as a present address.
The original article has been updated.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Keywords: essential genes/proteins, machine learning, systems biology, prediction models, network topological features
Citation: Zhang X, Acencio ML and Lemke N (2016) Corrigendum: Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review. Front. Physiol. 7:617. doi: 10.3389/fphys.2016.00617
Received: 01 November 2016; Accepted: 25 November 2016;
Published: 06 December 2016.
Edited and reviewed by: Josselin Noirel, Conservatoire National des Arts et Métiers, France
Copyright © 2016 Zhang, Acencio and Lemke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
†Present Address: Xue Zhang, School of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, China
Marcio L. Acencio, Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway
‡These authors have contributed equally to this work.