ArticleBriefings in bioinformatics2024
TUnA: an uncertainty-aware transformer model for sequence-based protein-protein interaction prediction.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability.Nature biotechnology · 2026Article
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Rapid Proteome-Wide Discovery of Protein-Protein Interactions With ppIRIS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks.PLoS computational biology · 2026Article
- DSS-PPI: a self-supervised graph learning framework for protein-protein interaction prediction via multimodal sequence semantics.BMC genomics · 2026Article
- A paired sequence language model for protein-protein interaction modeling.Nature communications · 2026Article
- DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction.ACS omega · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.Briefings in bioinformatics · 2026Article
- Multimodal graph, surface, and language-based model for protein protein interaction prediction.Scientific reports · 2026Article
- ProtAttn-QuadNet: An attention-based deep learning framework for protein-protein interaction prediction using ProtBERT embeddings.PloS one · 2026Article
- Dual Cross-Attention Network for Hierarchical Feature Fusion in Protein-Protein Interaction Prediction.Computational and structural biotechnology journal · 2026Article
- IHIT-BED: an interpretable transformer approach using unbiased hematology analyzer impedance data for early identification of bacteremia in emergency department.Bioinformatics advances · 2026Article
- An uncertainty-driven gated feature selection network (UGFS-Net) for TG level prediction: linking high-altitude exposure to lipid metabolism disorder via elevated TG.Lipids in health and disease · 2025Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.bioRxiv : the preprint server for biology · 2025Article
- Article
- Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface.bioRxiv : the preprint server for biology · 2025Article
- PPISHES-an enhanced physicochemical approach for predicting protein interaction sites using graph neural networks.Protein science : a publication of the Protein Society · 2025Article
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4 authors.
Funding
Abstract
Protein-protein interactions (PPIs) are important for many biological processes, but predicting them from sequence data remains challenging. Existing deep learning models often cannot generalize to proteins not present in the training set and do not provide uncertainty estimates for their predictions. To address these limitations, we present TUnA, a Transformer-based uncertainty-aware model for PPI prediction. TUnA uses ESM-2 embeddings with Transformer encoders and incorporates a Spectral-normalized Neural Gaussian Process. TUnA achieves state-of-the-art performance and, importantly, evaluates uncertainty for unseen sequences. We demonstrate that TUnA's uncertainty estimates can effectively identify the most reliable predictions, significantly reducing false positives. This capability is crucial in bridging the gap between computational predictions and experimental validation.
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