TY - JOUR
T1 - Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding
AU - Yuan, Qitong
AU - Chen, Keyi
AU - Yu, Yimin
AU - Le, Nguyen Quoc Khanh
AU - Chua, Matthew Chin Heng
N1 - Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].
PY - 2023/1/19
Y1 - 2023/1/19
N2 - Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.
AB - Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.
KW - Anticancer peptide
KW - Deep learning
KW - Feature fusion
KW - Handcrafted feature
KW - Machine learning
KW - Model ensemble
KW - Ordinal positional encoding
UR - http://www.scopus.com/inward/record.url?scp=85146579908&partnerID=8YFLogxK
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U2 - 10.1093/bib/bbac630
DO - 10.1093/bib/bbac630
M3 - Article
C2 - 36642410
AN - SCOPUS:85146579908
SN - 1467-5463
VL - 24
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 1
M1 - bbac630
ER -