ArticleCell systems2019
Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design.
Article in Cell systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- A bottleneck-first evaluation of antigen delivery platforms for therapeutic cancer vaccines.Materials today. Bio · 2026Review
- Benchmarking sequence-based and AlphaFold-based methods for pMHC-II binding core prediction: distinct strengths and consensus approaches.BMC medical genomics · 2026Article
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Identification of MHC Ligands Through Allele-Guided Isolation Combined With Machine Learning for Improved MHC Assignment Using ARDisplay-I.Molecular & cellular proteomics : MCP · 2026Article
- iPepGen: a modular, immunopeptidogenomic analysis pipeline for discovery, verification, and prioritization of cancer peptide neoantigen candidates.Genome biology · 2026Article
- The renaissance ofFrontiers in immunology · 2026Review
- Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology.Nature machine intelligence · 2025Article
- HLAIIPred: cross-attention mechanism for modeling the interaction of HLA class II molecules with peptides.Communications biology · 2025Article
- pMHChat, characterizing the interactions between major histocompatibility complex class II molecules and peptides with large language models and deep hypergraph learning.Briefings in bioinformatics · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- RNA-DNA Differences: Mechanisms, Oxidative Stress, Transcriptional Fidelity, and Health Implications.Antioxidants (Basel, Switzerland) · 2025Review
- Neoantigen-based immunotherapy: advancing precision medicine in cancer and glioblastoma treatment through discovery and innovation.Exploration of targeted anti-tumor therapy · 2025Review
- Neural network extrapolation to distant regions of the protein fitness landscape.Nature communications · 2024Article
- Identifying Bayesian optimal experiments for uncertain biochemical pathway models.Scientific reports · 2024Article
- Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity.Briefings in bioinformatics · 2024Article
- Forum on immune digital twins: a meeting report.NPJ systems biology and applications · 2024Review
- Structure-aware deep model for MHC-II peptide binding affinity prediction.BMC genomics · 2024Article
- RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The computational identification of peptides that can bind the major histocompatibility complex (MHC) with high affinity is an essential step in developing personal immunotherapies and vaccines. We introduce PUFFIN, a deep residual network-based computational approach that quantifies uncertainty in peptide-MHC affinity prediction that arises from observational noise and the lack of relevant training examples. With PUFFIN's uncertainty metrics, we define binding likelihood, the probability a peptide binds to a given MHC allele at a specified affinity threshold. Compared to affinity point estimates, we find that binding likelihood correlates better with the observed affinity and reduces false positives in high-affinity peptide design. When applied to examine an existing peptide vaccine, PUFFIN identifies an alternative vaccine formulation with higher binding likelihood. PUFFIN is freely available for download at http://github.com/gifford-lab/PUFFIN.
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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.