iANOP-Enble: a sequence-based ensemble classifier for identifying antioxidant proteins by PseAAC and Random Forests
- 10.2991/amcce-17.2017.103How to use a DOI?
- Antioxidant proteins; Voting system; Ensemble classifier
Inoxidizability of proteins is one of the most basic function attribute, and shares a sustainable effect for biological process in protein repair and regulate redox-sensitive signaling pathways. In the genome era, however, it is urgent to design an effective computation method to rapidly detect the antioxidant proteins based on sequence information due to the addition of the larger amount of sequence. We designed a novel automations computational algorithm named "iANOP-Enble". In this predictor, the protein sample was formulated by protein similarity scores matrix and amino acid prosperities information into Random Forest. The process of the new predictor algorithm to identify antioxidants protein is designed as a voting system, which consists of eleven sub-classifiers. In order to verify our algorithm availabilities, we adopted a fair comparison method that used the same bench data set. Finally, the result shows that our algorithm is more promising than existing method on the basis of the same standard of comparison
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xuan Xiao AU - Weifeng Ju AU - Mengjuan Hui PY - 2017/03 DA - 2017/03 TI - iANOP-Enble: a sequence-based ensemble classifier for identifying antioxidant proteins by PseAAC and Random Forests BT - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017) PB - Atlantis Press SP - 587 EP - 593 SN - 2352-5401 UR - https://doi.org/10.2991/amcce-17.2017.103 DO - 10.2991/amcce-17.2017.103 ID - Xiao2017/03 ER -