Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model

Qingwen Li; Wenyang Zhou; Donghua Wang; Sui Wang; Qingyuan Li

doi:10.3389/fbioe.2020.00892

Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model

Front Bioeng Biotechnol. 2020 Aug 12:8:892. doi: 10.3389/fbioe.2020.00892. eCollection 2020.

Authors

Qingwen Li¹, Wenyang Zhou², Donghua Wang³, Sui Wang^{4

5}, Qingyuan Li⁶

Affiliations

¹ College of Animal Science and Technology, Northeast Agricultural University, Harbin, China.
² Center for Bioinformatics, School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China.
³ Department of General Surgery, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China.
⁴ Key Laboratory of Soybean Biology in Chinese Ministry of Education, Northeast Agricultural University, Harbin, China.
⁵ State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China.
⁶ Forestry and Fruit Tree Research Institute, Wuhan Academy of Agricultural Sciences, Wuhan, China.

Abstract

Cancer is still a severe health problem globally. The therapy of cancer traditionally involves the use of radiotherapy or anticancer drugs to kill cancer cells, but these methods are quite expensive and have side effects, which will cause great harm to patients. With the find of anticancer peptides (ACPs), significant progress has been achieved in the therapy of tumors. Therefore, it is invaluable to accurately identify anticancer peptides. Although biochemical experiments can solve this work, this method is expensive and time-consuming. To promote the application of anticancer peptides in cancer therapy, machine learning can be used to recognize anticancer peptides by extracting the feature vectors of anticancer peptides. Nevertheless, poor performance usually be found in training the machine learning model to utilizing high-dimensional features in practice. In order to solve the above job, this paper put forward a 19-dimensional feature model based on anticancer peptide sequences, which has lower dimensionality and better performance than some existing methods. In addition, this paper also separated a model with a low number of dimensions and acceptable performance. The few features identified in this study may represent the important features of anticancer peptides.

Keywords: anticancer peptide; feature extraction; feature model; feature selection; machine learning.