Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model

• Main Authors: Qingwen Li, Wenyang Zhou, Donghua Wang, Sui Wang, Qingyuan Li
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-08-01
• Series: Frontiers in Bioengineering and Biotechnology
• Subjects: anticancer peptide; feature extraction; feature model; feature selection; machine learning
• Online Access: https://www.frontiersin.org/article/10.3389/fbioe.2020.00892/full

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.