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Combinatorial Chemistry & High Throughput Screening

Author(s): Wanben Zhong, Bineng Zhong*, Hongbo Zhang, Ziyi Chen and Yan Chen

DOI: 10.2174/1386207322666191203141102

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

Page: [694 - 704] Pages: 11

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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.

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