DRACP: a novel method for identification of anticancer peptides

• Published in: BMC bioinformatics Vol. 21; no. Suppl 16; p. 559
• Main Authors: Zhao, Tianyi, Hu, Yang, Zang, Tianyi
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 16.12.2020; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Amino acids; Analysis; Anticancer peptides; Antineoplastic Agents - therapeutic use; Belief networks; Cancer; Cancer therapies; Chemotherapy; Computational Biology - methods; Datasets; Deep belief network; Feature extraction; Humans; Hydrophobicity; Identification and classification; Identification methods; Learning algorithms; Machine Learning; Methods; Neoplasms - drug therapy; Peptides; Peptides - chemistry; Peptides - therapeutic use; Power; Radiation therapy; Random forest; Relevance vector machine; ROC Curve; Support Vector Machine; China; Anticancer peptides; Random forest; Deep belief network; Relevance vector machine; Cancer
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-03812-y

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. 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. We developed a novel method named 'DRACP' and compared it with some traditional methods. The cross-validation results showed its effectiveness in identifying ACPs.