PTPD: predicting therapeutic peptides by deep learning and word2vec

Wu, Chuanyan; Gao, Rui; Zhang, Yusen; Marinis, Yang De

doi:10.1186/s12859-019-3006-z

Cited by 87 publications

(72 citation statements)

References 37 publications

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“…Its ability to represent implicit relationships between words has resulted in substantial machine learning improvements on domains by contextual information. Some examples include the classification of news articles and tweets [31] , the analysis of biological data for the prediction of therapeutic peptides [32] , the detection of malware activity on Android devices [33] , and the recommendation of contents in social networks [34] . Similarly to these studies, the method proposed in this paper leverages Word2Vec as a method to extract word embeddings.…”

Section: Methodsmentioning

confidence: 99%

Feature extraction based on word embedding models for intrusion detection in network traffic

Corizzo¹,

Zdravevski²,

Russell³

et al. 2020

JSSS

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Section: Methodsmentioning

confidence: 99%

Feature extraction based on word embedding models for intrusion detection in network traffic

Corizzo¹,

Zdravevski²,

Russell³

et al. 2020

JSSS

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“…To overcome this limitation, deep learning algorithms are used to further improve the prediction accuracy and robustness. 27 , 28 , 29 Wu et al. 27 proposed a computational model based on convolutional neural networks and word2vec to predict therapeutic peptides in a highly efficient manner.…”

Section: Introductionmentioning

confidence: 99%

DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm

Jing

Liu

et al. 2020

Molecular Therapy - Nucleic Acids

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Cancer is one of the most dangerous diseases to human health. The accurate prediction of anticancer peptides (ACPs) would be valuable for the development and design of novel anticancer agents. Current deep neural network models have obtained state-of-the-art prediction accuracy for the ACP classification task. However, based on existing studies, it remains unclear which deep learning architecture achieves the best performance. Thus, in this study, we first present a systematic exploration of three important deep learning architectures: convolutional, recurrent, and convolutional-recurrent networks for distinguishing ACPs from non-ACPs. We find that the recurrent neural network with bidirectional long short-term memory cells is superior to other architectures. By utilizing the proposed model, we implement a sequence-based deep learning tool (DeepACP) to accurately predict the likelihood of a peptide exhibiting anticancer activity. The results indicate that DeepACP outperforms several existing methods and can be used as an effective tool for the prediction of anticancer peptides. Furthermore, we visualize and understand the deep learning model. We hope that our strategy can be extended to identify other types of peptides and may provide more assistance to the development of proteomics and new drugs.

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“…With the development of deep learning, distributed representation method has made great achievements in the Natural Language Processing field, such as word2vec [3], and GloVe [4], which has received great attention and been widely used [5,6]. Naturally, the protein sequence was considered as biology "sentence" and "amino acid vectors" were obtained by word2vec, which could represent the protein sequence for further protein studies [7,8]. However, the evolutionary information was not included in the existing methods, though it is very important for protein analysis.…”

Section: Introductionmentioning

confidence: 99%

Distributed Representation of Protein Sequence Based on Multi-Alignment Results

et al. 2020

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Protein sequence representation is a key problem for protein studies, especially for those sequence-based models. In this paper, a distributed representation model of protein sequence is proposed, which involves evolutionary information by introducing multi-alignment results. Firstly, we construct a non-redundancy protein dataset and perform multi-alignment for each protein. Then k-mer amino acids "biology corpus" was abstracted from the alignment results which are "evolutionary information" enriched. Using the "biology corpus", k-mer amino acids distributed embedding vectors could be trained according to word2vec method. We compared the amino acid pair distance derived from our produced 1-mer amino acids distributed embedding vectors with that derived from BLOSUM62; it was found that their Pearson coefficient is 0.937, showing they have strong correlation. Then we applied the obtained amino acids distributed embedding representation to protein secondary structure recognition and solubility prediction. For both of the experiments, our proposed alignment results based amino acid distributed representation outperforms that derived directly from protein sequences. Moreover, compared to those existing up-to-date algorithms, our method could get better or comparative results, on condition of only using the feature of our produced amino acid distributed vectors.

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PTPD: predicting therapeutic peptides by deep learning and word2vec

Cited by 87 publications

References 37 publications

Feature extraction based on word embedding models for intrusion detection in network traffic

Feature extraction based on word embedding models for intrusion detection in network traffic

DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm

Distributed Representation of Protein Sequence Based on Multi-Alignment Results

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