ArticleBMC bioinformatics2019
PTPD: predicting therapeutic peptides by deep learning and word2vec.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Taxonomic quasi-primes: peptides charting lineage-specific adaptations and disease-relevant loci.Protein science : a publication of the Protein Society · 2025Article
- GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi-source feature embeddings.Protein science : a publication of the Protein Society · 2025Article
- Article
- Unraveling diversity by isolating peptide sequences specific to distinct taxonomic groups.bioRxiv : the preprint server for biology · 2025Article
- pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.Scientific reports · 2025Article
- Application of machine learning in the discovery of antimicrobial peptides: exploring their potential for ulcerative colitis therapy.eGastroenterology · 2025Article
- AttentionEP: Predicting essential proteins via fusion of multiscale features by attention mechanisms.Computational and structural biotechnology journal · 2024Article
- MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism.Protein science : a publication of the Protein Society · 2024Article
- Contrastive learning for enhancing feature extraction in anticancer peptides.Briefings in bioinformatics · 2024Article
- An efficient consolidation of word embedding and deep learning techniques for classifying anticancer peptides: FastText+BiLSTM.PeerJ. Computer science · 2024Article
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Article
- ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information.International journal of molecular sciences · 2023Article
- An Augmented Sample Selection Framework for Prediction of Anticancer Peptides.Molecules (Basel, Switzerland) · 2023Article
- Impact of word embedding models on text analytics in deep learning environment: a review.Artificial intelligence review · 2023Article
- Anti-Cancer Peptides: Status and Future Prospects.Molecules (Basel, Switzerland) · 2023Review
- Identification of Relevant Protein Interactions with Partial Knowledge: A Complex Network and Deep Learning Approach.Biology · 2023Article
- NeuroCNN_GNB: an ensemble model to predict neuropeptides based on a convolution neural network and Gaussian naive Bayes.Frontiers in genetics · 2023Article
- ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction.Scientific reports · 2022Article
- Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anticancer Peptides.ChemMedChem · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational model that uses deep learning and word2vec to predict therapeutic peptides (PTPD). *: Results Representation vectors of all k-mers were obtained through word2vec based on k-mer co-existence information. The original peptide sequences were then divided into k-mers using the windowing method. The peptide sequences were mapped to the input layer by the embedding vector obtained by word2vec. Three types of filters in the convolutional layers, as well as dropout and max-pooling operations, were applied to construct feature maps. These feature maps were concatenated into a fully connected dense layer, and rectified linear units (ReLU) and dropout operations were included to avoid over-fitting of PTPD. The classification probabilities were generated by a sigmoid function. PTPD was then validated using two datasets: an independent anticancer peptide dataset and a virulent protein dataset, on which it achieved accuracies of 96% and 94%, respectively. *: Conclusions PTPD identified novel therapeutic peptides efficiently, and it is suitable for application as a useful tool in therapeutic peptide design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.