ArticleBioinformatics (Oxford, England)2023
Attentive Variational Information Bottleneck for TCR-peptide interaction prediction.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformers meets neoantigen detection: a systematic literature review.Journal of integrative bioinformatics · 2024Pooled it
- Learning the language of protein-protein interactions.Nature communications · 2026Article
- Assessment of computational methods in predicting TCR-epitope binding recognition.Nature methods · 2026Article
- HLA alleles shape distinct biases in the usage preferences of TCR VFrontiers in immunology · 2026Article
- Learning the language of protein-protein interactions.bioRxiv : the preprint server for biology · 2025Article
- Assessing the generalization capabilities of TCR binding predictors via peptide distance analysis.PloS one · 2025Article
- Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics.bioRxiv : the preprint server for biology · 2024Article
- NeoAgDT: optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population.Bioinformatics (Oxford, England) · 2024Article
- Review
- epiTCR-KDA: knowledge distillation model on dihedral angles for TCR-peptide prediction.Bioinformatics advances · 2024Article
- TCRcost: a deep learning model utilizing TCR 3D structure for enhanced of TCR-peptide binding.Frontiers in genetics · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationWe present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T-cell receptors (TCRs) and peptides.
resultsExperimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution amino acid sequences. AVAILABILITY AND IMPLEMENTATION: The code and the data used for this study are publicly available at: https://github.com/nec-research/vibtcr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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