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In Combinatorial chemistry & high throughput screening ; h5-index 0.0

AIM AND OBJECTIVE : Cancer is one of the world's killers, killing millions of lives every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anticancer peptides(ACPs) can eliminate this side effect. However, the identification and development of the new anticancer peptides through experiment takes a lot of time and money, so it is necessary to develop a fast and accurate calculation model to identify the anti-cancer peptide. Machine learning algorithms are good choices.

MATERIALS AND METHODS : In our study, we use a multi-classifier system, which combining multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and then form a multi-classifier system by voting.

RESULTS AND CONCLUSION : 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.

Zhong Wanben, Zhong Bineng, Zhang Hongbo, Chen Ziyi, Chen Yan


Anti-Cancer Peptides, Feature Extraction, Individual Learner, Machine Learning, Multi-Classifier System, Prediction Model