ArticleBMC genomics2017
SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- SSEL-CPP: A SHAP-based feature-selection ensemble learning framework identifies molecular properties of cell-penetrating peptides.Protein science : a publication of the Protein Society · 2026Article
- Enhancing tumor T cell antigen prediction by integrating deep protein representations.BMC bioinformatics · 2026Article
- CPP2Vec: A representation learning approach for cell-penetrating peptides prediction.PLoS computational biology · 2026Article
- Design and classify innovative antimicrobial and dual- and multi-functional peptides using generative artificial intelligence.Scientific reports · 2026Article
- Integrative Peptide Drug Development: Chemical Engineering, AI-Driven Design, and Cell-Penetrating Peptides.Pharmaceutics · 2026Review
- Leveraging a Meta-Learning Strategy to Advance the Accuracy of Neutralizing Antibodies against Dengue Virus Serotype Prediction.ACS omega · 2025Article
- EnDM-CPP: A Multi-view Explainable Framework Based on Deep Learning and Machine Learning for Identifying Cell-Penetrating Peptides with Transformers and Analyzing Sequence Information.Interdisciplinary sciences, computational life sciences · 2025Article
- AOPxSVM: A Support Vector Machine for Identifying Antioxidant Peptides Using a Block Substitution Matrix and Amino Acid Composition, Transformation, and Distribution Embeddings.Foods (Basel, Switzerland) · 2025Article
- PerseuCPP: a machine learning strategy to predict cell-penetrating peptides and their uptake efficiency.Bioinformatics advances · 2025Article
- Exploring the Chemical Features and Biomedical Relevance of Cell-Penetrating Peptides.International journal of molecular sciences · 2024Review
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications.Scientific reports · 2024Article
- TriplEP-CPP: Algorithm for Predicting the Properties of Peptide Sequences.International journal of molecular sciences · 2024Article
- POSEIDON: Peptidic Objects SEquence-based Interaction with cellular DOmaiNs: a new database and predictor.Journal of cheminformatics · 2024Article
- PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction.Bioinformatics (Oxford, England) · 2024Article
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- A bird's-eye view of the biological mechanism and machine learning prediction approaches for cell-penetrating peptides.Frontiers in artificial intelligence · 2024Review
- Advancements in mitochondrial-targeted nanotherapeutics: overcoming biological obstacles and optimizing drug delivery.Frontiers in immunology · 2024Review
- Purification and characterization of a novel immunoregulatory peptide fromFood science & nutrition · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCell-penetrating peptides (CPPs) are short peptides (5-30 amino acids) that can enter almost any cell without significant damage. On account of their high delivery efficiency, CPPs are promising candidates for gene therapy and cancer treatment. Accordingly, techniques that correctly predict CPPs are anticipated to accelerate CPP applications in future therapeutics. Recently, computational methods have been reportedly successful in predicting CPPs. Unfortunately, the predictive performance of existing methods is not satisfactory and reliable so as to accurately identify CPPs.
resultsIn this study, we propose a novel computational predictor called SkipCPP-Pred to further improve the predictive performance. The novelty of the proposed predictor is that we present a sequence-based feature representation algorithm called adaptive k-skip-n-gram that sufficiently captures the intrinsic correlation information of residues. By fusing the proposed adaptive skip features with a random forest (RF) classifier, we successfully construct the prediction model of SkipCPP-Pred. The various jackknife results demonstrate that the proposed SkipCPP-Pred is 3.6% higher than state-of-the-art CPP predictors in terms of accuracy. Moreover, we construct a high-quality benchmark dataset by reducing the data redundancy and enhancing the similarity between the positive and negative classes. Using this dataset to build prediction models, we can successfully avoid the performance bias lying in existing methods and yield a promising predictive model.
conclusionsThe proposed SkipCPP-Pred is a simple and fast sequence-based predictor featured with the adaptive k-skip-n-gram model for the improved prediction of CPPs. Currently, SkipCPP-Pred is publicly available from an online webserver ( http://server.malab.cn/SkipCPP-Pred/Index.html ).
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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.