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Identification of Anti-cancer Peptides Based on Multi-classifier System

[ Vol. 22 , Issue. 10 ]

Author(s):

Wanben Zhong, Bineng Zhong*, Hongbo Zhang, Ziyi Chen and Yan Chen   Pages 694 - 704 ( 11 )

Abstract:


Aims and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti-cancer peptides through experiments take a lot of time and money, therefore, it is necessary to develop a fast and accurate calculation model to identify the anti-cancer peptide. Machine learning algorithms are a good choice.

Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting.

Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

Keywords:

Anti-cancer peptides, machine learning, individual learner, feature extraction, multi-classifier system, prediction model.

Affiliation:

School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021, School of Computer Science and Technology, Huaqiao University, Xiamen, Fujian, 361021



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