iANOP-Enble: a sequence-based ensemble classifier for identifying antioxidant proteins by PseAAC and Random Forests

DOI

10.2991/amcce-17.2017.103How to use a DOI?

Keywords

Antioxidant proteins; Voting system; Ensemble classifier

Abstract

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

Copyright

© 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/).

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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  -