EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides

Frontiers in Genetics

Pu Wang¹  Ruiquan Ge²  Qing Wu²  Renfeng Zhang³  Xiaoyang Jing⁴  Guanwen Feng⁵ 

[1] Computer School, Hubei University of Arts and Science, Xiangyang, China;Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China;Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China;Toyota Technological Institute at Chicago, Chicago, IL, United States;Xi'an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi'an, China;

关键词: anticancer peptides;    feature representation;    ensemble learning;    pseudo amino acid composition;    system biology;   

DOI  :  10.3389/fgene.2020.00760

来源: DOAJ

【 摘 要 】

As cancer remains one of the main threats of human life, developing efficient cancer treatments is urgent. Anticancer peptides, which could overcome the significant side effects and poor results of traditional cancer treatments, have become a new potential alternative these years. However, identifying anticancer peptides by experimental methods is time consuming and resource consuming, it is of great significance to develop effective computational tools to quickly and accurately identify potential anticancer peptides from amino acid sequences. For most current computational methods, feature representation plays a key role in their final successes. This study proposes a novel fast and accurate approach to identify anticancer peptides using diversified feature representations and ensemble learning method. For the feature representations, the information is encoded from multidimensional feature spaces, including sequence composition, sequence-order, physicochemical properties, etc. In order to better model the potential relationships of peptides, multiple ensemble classifiers, LightGBMs, are applied to detect the different feature sets at first. Then the obtained multiple outputs are used as inputs of the support vector machine classifier, which effectively identifies anticancer peptides. Experimental results on cross validation and independent test sets demonstrate that our method can achieve better or comparable performances compared with other state-of-the-art methods.

【 授权许可】

Unknown