2021
DOI: 10.1038/s41598-021-02703-3
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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 alternativ… Show more

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Cited by 46 publications
(33 citation statements)
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“…In order to verify whether the ensemble deep learning strategy of PD-BertEDL method can effectively solve the heterogeneity problem among different information and improve the prediction performance of peptide detectability, we designed and implemented three methods: Linear Fusion [ 26 ], Same Network [ 27 ], and Hybrid [ 38 , 39 , 40 ], respectively. Among them, Linear Fusion linearly spliced the three kinds of peptide information used in this paper, input the spliced vector into CNN+BiLSTM network to extract features and used softmax to classify.…”
Section: Resultsmentioning
confidence: 99%
“…In order to verify whether the ensemble deep learning strategy of PD-BertEDL method can effectively solve the heterogeneity problem among different information and improve the prediction performance of peptide detectability, we designed and implemented three methods: Linear Fusion [ 26 ], Same Network [ 27 ], and Hybrid [ 38 , 39 , 40 ], respectively. Among them, Linear Fusion linearly spliced the three kinds of peptide information used in this paper, input the spliced vector into CNN+BiLSTM network to extract features and used softmax to classify.…”
Section: Resultsmentioning
confidence: 99%
“…In addition, these feature extraction methods can only extract the local order of peptide sequence through features such as DPC 23 , and it is difficult to grasp the global order information. Finally, the performance of these methods is largely related to manual feature extraction mechanisms, but it is not easy to extract suitable features for different data 26 .…”
Section: Introductionmentioning
confidence: 99%
“…Yi et al 28 proposed ACP-DL in 2019, which uses BPF and k-mer sparse matrix feature to represent the peptide sequences, and uses Long Short-Term Memory Model (LSTM) for prediction. Unlike traditional machine learning-based methods, deep learning-based methods do not require manual feature extraction to represent the input data 26 , that is, they can automatically extract features 29 . The methods based on deep learning can be divided into two categories: one uses deep learning methods to extract features, and then inputs the features into traditional machine learning classifiers such as SVM and RF for prediction; the other directly uses the deep learning method to make the final prediction.…”
Section: Introductionmentioning
confidence: 99%
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