DRACP: a novel method for identification of anticancer peptides

• Main Authors: Tianyi Zhao, Yang Hu, Tianyi Zang
• Format: Article
• Language: English
• Published: BMC 2020-12-01
• Series: BMC Bioinformatics
• Subjects: Anticancer peptides; Deep belief network; Relevance vector machine; Random forest; Cancer
• Online Access: https://doi.org/10.1186/s12859-020-03812-y

Abstract Background Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. Common ways against cancer include surgical operation, radiotherapy and chemotherapy. However, they are all very harmful for patients. Recently, the anticancer peptides (ACPs) have been discovered to be a potential way to treat cancer. Since ACPs are natural biologics, they are safer than other methods. However, the experimental technology is an expensive way to find ACPs so we purpose a new machine learning method to identify the ACPs. Results Firstly, we extracted the feature of ACPs in two aspects: sequence and chemical characteristics of amino acids. For sequence, average 20 amino acids composition was extracted. For chemical characteristics, we classified amino acids into six groups based on the patterns of hydrophobic and hydrophilic residues. Then, deep belief network has been used to encode the features of ACPs. Finally, we purposed Random Relevance Vector Machines to identify the true ACPs. We call this method ‘DRACP’ and tested the performance of it on two independent datasets. Its AUC and AUPR are higher than 0.9 in both datasets. Conclusion We developed a novel method named ‘DRACP’ and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.