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Genes 2018, 9(3), 158; https://doi.org/10.3390/genes9030158

A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides

1
School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen 518060, China
2
Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Received: 24 January 2018 / Revised: 14 February 2018 / Accepted: 27 February 2018 / Published: 13 March 2018
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Abstract

Cancer is a serious health issue worldwide. Traditional treatment methods focus on killing cancer cells by using anticancer drugs or radiation therapy, but the cost of these methods is quite high, and in addition there are side effects. With the discovery of anticancer peptides, great progress has been made in cancer treatment. For the purpose of prompting the application of anticancer peptides in cancer treatment, it is necessary to use computational methods to identify anticancer peptides (ACPs). In this paper, we propose a sequence-based model for identifying ACPs (SAP). In our proposed SAP, the peptide is represented by 400D features or 400D features with g-gap dipeptide features, and then the unrelated features are pruned using the maximum relevance-maximum distance method. The experimental results demonstrate that our model performs better than some existing methods. Furthermore, our model has also been extended to other classifiers, and the performance is stable compared with some state-of-the-art works. View Full-Text
Keywords: anticancer peptides; sequence-based method; g-gap dipeptide; 400D; dimension reduction anticancer peptides; sequence-based method; g-gap dipeptide; 400D; dimension reduction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Xu, L.; Liang, G.; Wang, L.; Liao, C. A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides. Genes 2018, 9, 158.

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