ArticleScientific reports2016
sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 27 citations in OpenAlex.
- Interpretable machine learning models for predicting the antitumor effects of metal and metal oxide nanomaterials.RSC advances · 2025Article
- DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.Frontiers in immunology · 2023Article
- Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.Briefings in bioinformatics · 2021Article
- DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction.Scientific reports · 2019Article
- DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.Frontiers in immunology · 2019Article
- In silico design of MHC class I high binding affinity peptides through motifs activation map.BMC bioinformatics · 2018Article
- TSNAdb: A Database for Tumor-specific Neoantigens from Immunogenomics Data Analysis.Genomics, proteomics & bioinformatics · 2018Article
- PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity.GigaScience · 2017Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.
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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.