ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

Abstract

Anticancer peptides (ACPs) have provided a promising perspective for cancer treatment, and the prediction of ACPs is very important for the discovery of new cancer treatment drugs. It is time consuming and expensive to use experimental methods to identify ACPs, so computational methods for ACP identification are urgently needed. There have been many effective computational methods, especially machine learning-based methods, proposed for such predictions. Most of the current machine learning methods try to find suitable features or design effective feature learning techniques to accurately represent ACPs. However, the performance of these methods can be further improved for cases with insufficient numbers of samples. In this article, we propose an ACP prediction model called ACP-DA (Data Augmentation), which uses data augmentation for insufficient samples to improve the prediction performance. In our method, to better exploit the information of peptide sequences, peptide sequences are represented by integrating binary profile features and AAindex features, and then the samples in the training set are augmented in the feature space. After data augmentation, the samples are used to train the machine learning model, which is used to predict ACPs. The performance of ACP-DA exceeds that of existing methods, and ACP-DA achieves better performance in the prediction of ACPs compared with a method without data augmentation. The proposed method is available at http://github.com/chenxgscuec/ACPDA.

ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides

“…To ensure the effectiveness and efficiency of the proposed method, we compared the performance of ACP-ADA with ACP-DA [ 19 ], ACP-DL [ 17 ], AntiCP2.0 [ 18 ], and DeepACP [ 15 ] while relying on the same main and benchmark datasets and corresponding classification evaluation metrics.…”

“…AntiCP 2.0, an updated model for the prediction of ACPs using various features and different classes of machine learning classifiers on two datasets-ACP740 and ACP240, has been proposed for the prediction of ACPs [ 18 ]. A data augmentation method named ACP-DA, which uses sequential features and a multi-layer perceptron (MLP) classifier to predict ACPs using sequential physiocochemical features, has been proposed as well [ 19 ].…”

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

“…Especially, in DeepACP, CNN, RNN, and CNN-RNN models were compared, and RNN showed the best performance [ 63 ]. Additionally, hybrid learning is used for ACP development in ACP-DA [ 65 ] and by Lv et al [ 25 ]. AI tools for ACP prediction are summarized in Table 2 .…”

“…In Lv et al’s hybrid learning process, data split, embedding, and feature extraction are performed by the DL method, and classification is performed by the ML method [ 25 ]. On the other hand, in ACP-DA, data split and feature extraction are performed by the ML method, and classification is performed by the DL method [ 65 ]. After classification in AI methods, prediction is performed [ 144 ].…”

ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information

“…In 2019,Yi Haichent et al proposed a deep learning long short-term memory (LSTM) neural network model, called ACP-DL [ 10 ], which developed an efficient feature representation approach by integrating binary profile features, k-mer sparse matrix of the reduced amino acid alphabet and then implemented a deep LSTM model to identify ACPs. In 2021, Chen Xiangan et al proposed an ACP prediction model, called ACP-DA [ 11 ], which uses data augmentation for insufficient samples and trains a multilayer perception model to improve the prediction performance. In 2020, Yu Lezheng et al found that the recurrent neural network with bidirectional long short-term memory cells is a superior architecture to identify ACPs and implement a sequence-based deep learning tool, called DeepACP [ 12 ], to accurately predict ACPs.…”