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.