DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion

PeerJ

article

Ruifen Cao¹  Meng Wang¹  Yannan Bin¹  Chunhou Zheng¹ 

[1] Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University;Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University;Institutes of Physical Science and Information Technology, Anhui University

关键词: Anticancer peptide;    Deep learning;    Handcrafted feature;    Features fusion;    Prediction;   

DOI  :  10.7717/peerj.11906

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model’s predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model’s area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.

【 授权许可】

CC BY