ACP-MHCNN: An Accurate Multi-Headed Deep-Convolutional Neural Network to Predict Anticancer peptides

Abstract

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification through simultaneous interaction with different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN.

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

“…To verify the performance of our proposed model, we compared it with six traditional machine learning methods: AntiCP ( Tyagi et al, 2013 ), Hajisharifi’s method ( Hajisharifi et al, 2014 ), iACP ( Chen et al, 2016 ), ACPredFL ( Wei et al, 2018 ), PEPred-Suite ( Wei et al, 2019 ), ACPred_Fuse ( Rao et al, 2020b ), and two deep learning methods, DeepACP ( Yu et al, 2020 ) and ACP-MHCNN ( Ahmed et al, 2020 ). We used the results of traditional machine learning method comparisons from the literature ( Rao et al, 2020b ).…”

“… Yu et al (2020) compared three different DNN architectures and found that the best model was based on bidirectional long short-term memory cells. Ahmed et al (2020) constructed a new DNN architecture using parallel convolution groups to learn and combine three different features. Rao, Zhang & G (2020a) was the first to apply graph convolutional networks in ACP prediction.…”

Active Semisupervised Model for Improving the Identification of Anticancer Peptides

“…To validate the superiority of ACP-ALPM, we compared its performance with some state-of-the-art methods, including AntiCP, 14 Hajisharifi's method, 15 iACP, 34 ACPred-FL, 4 DeepACP, 35 PEPred-Suite, 36 ACPred-Fuse, 7 ACP-DL, 17 and ACP-MHCNN. 19 Among them, AntiCP represents two predictors composed of amino acids and dipeptides, and DeepACP represents the classifiers generated by the recurrent neural network. For a fair comparison, all approaches were trained and tested on the ACP500 data sets.…”

“…In addition, Liang et al 18 improved potential models by conducting comparative experiments on classic models, such as SVM, Naive Bayesian, Light Gradient Boosting Machine (lightGBM), and k-nearest neighbors (KNNs). Recently, Muhammod et al 19 proposed a new multihead deep convolutional neural network model, which further leveraged deep learning for ACP identification and obtained excellent experimental results. The last few years have witnessed the development of computation-based methods, especially related to ML.…”