ArticleBMC genomics2017
DRREP: deep ridge regressed epitope predictor.
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 20 papers.
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Who cites it
20 citing papers in PubMed.
- Article
- Review
- DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability.Computational and structural biotechnology journal · 2024Article
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 2024Review
- Multi-perspectives and challenges in identifying B-cell epitopes.Protein science : a publication of the Protein Society · 2023Review
- Leveraging deep learning to improve vaccine design.Trends in immunology · 2023Review
- B-Cell Epitope Predictions Using Computational Methods.Methods in molecular biology (Clifton, N.J.) · 2023Article
- NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes.Genomics, proteomics & bioinformatics · 2022Article
- Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review.Pathogens (Basel, Switzerland) · 2022Review
- DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity.Computational and structural biotechnology journal · 2022Article
- Advances in antibody discovery from human BCR repertoires.Frontiers in bioinformatics · 2022Review
- Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface.International journal of molecular sciences · 2021Review
- An integrated in silico immuno-genetic analytical platform provides insights into COVID-19 serological and vaccine targets.Genome medicine · 2021Article
- Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion.Computational and structural biotechnology journal · 2021Review
- Identification and Analysis of Unstructured, Linear B-Cell Epitopes in SARS-CoV-2 Virion Proteins for Vaccine Development.Vaccines · 2020Article
- A Review of Deep Learning Methods for Antibodies.Antibodies (Basel, Switzerland) · 2020Review
- Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.Frontiers in artificial intelligence · 2020Review
- Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting.PeerJ · 2019Article
- Review
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2016: summary and innovation in genomics.BMC genomics · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
introductionThe ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP).
resultsDRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702.
conclusionDRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.
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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.