ArticleCommunications biology2024
Building trust in deep learning-based immune response predictors with interpretable explanations.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Organoid intelligence: a promising paradigm for autism spectrum disorder research.Molecular psychiatry · 2026Review
- Computational Design of a Broad-Spectrum Multi-Epitope mRNA Vaccine Candidate for Mammarenaviruses.Applied biochemistry and biotechnology · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 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
- Computational models for the classification of antibody specificity using heavy chain features.PloS one · 2026Article
- The ExoGAN generative AI framework enables extracellular vesicle-based immunotherapy.Research square · 2025Article
- Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.Cancer innovation · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The ability to predict whether a peptide will get presented on Major Histocompatibility Complex (MHC) class I molecules has profound implications in designing vaccines. Numerous deep learning-based predictors for peptide presentation on MHC class I molecules exist with high levels of accuracy. However, these MHC class I predictors are treated as black-box functions, providing little insight into their decision making. To build turst in these predictors, it is crucial to understand the rationale behind their decisions with human-interpretable explanations. We present MHCXAI, eXplainable AI (XAI) techniques to help interpret the outputs from MHC class I predictors in terms of input peptide features. In our experiments, we explain the outputs of four state-of-the-art MHC class I predictors over a large dataset of peptides and MHC alleles. Additionally, we evaluate the reliability of the explanations by comparing against ground truth and checking their robustness. MHCXAI seeks to increase understanding of deep learning-based predictors in the immune response domain and build trust with validated explanations.
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What OpenQuestion holds
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.