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BioMed Research International
Volume 2016, Article ID 6802832, 10 pages
http://dx.doi.org/10.1155/2016/6802832
Research Article

ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier

1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
2Shanghai University of Medicine & Health Sciences, Shanghai 201318, China

Received 1 June 2016; Revised 15 July 2016; Accepted 7 August 2016

Academic Editor: Dariusz Mrozek

Copyright © 2016 Daozheng Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [6 citations]

The following is the list of published articles that have cited the current article.

  • Pedro Shiguihara-Juarez, David Mauricio-Sanchez, and Alneu de Andrade Lopes, “Approaches based on tree-structures classifiers to protein fold prediction,” 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1–4, . View at Publisher · View at Google Scholar
  • Leyi Wei, and Quan Zou, “Recent progress in machine learning-based methods for protein fold recognition,” International Journal of Molecular Sciences, vol. 17, no. 12, 2016. View at Publisher · View at Google Scholar
  • Md. Al Mehedi Hasan, Shamim Ahmad, Md. Khademul Islam Molla, and Jinyan Li, “predCar-site: Carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue,” Analytical Biochemistry, vol. 525, pp. 107–113, 2017. View at Publisher · View at Google Scholar
  • Ke Yan, Yong Xu, Xiaozhao Fang, Chunhou Zheng, and Bin Liu, “Protein fold recognition based on sparse representation based classification,” Artificial Intelligence in Medicine, 2017. View at Publisher · View at Google Scholar
  • Katarzyna Stapor, Irena Roterman-Konieczna, and Piotr Fabian, “Machine Learning Methods for the Protein Fold Recognition Problem,” Machine Learning Paradigms, vol. 149, pp. 101–127, 2018. View at Publisher · View at Google Scholar
  • Sudong Lee, and Chi-Hyuck Jun, “Fast incremental learning of logistic model tree using least angle regression,” Expert Systems with Applications, vol. 97, pp. 137–145, 2018. View at Publisher · View at Google Scholar