ArticleBioinformatics advances2025
Aggregating residue-level protein language model embeddings with optimal transport.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Single-cell foundation modeling with species-native protein tokens links regenerative competence across frog and mouse.bioRxiv : the preprint server for biology · 2026Article
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- AlphaInterp: Mechanistic Interpretability of AlphaFold 3 Reveals How Evolutionary Information Shapes Protein Structure Prediction.bioRxiv : the preprint server for biology · 2026Article
- Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability.Scientific reports · 2026Article
- PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.PLoS computational biology · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.Briefings in bioinformatics · 2026Article
- Pooling multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal.Bioinformatics advances · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.bioRxiv : the preprint server for biology · 2025Article
- Evaluating Pretrained Protein Language Model Embeddings as Proxies for Functional Similarity.Journal of molecular evolution · 2025Article
- Paying attention to attention: High attention sites as indicators of protein family and function in language models.PLoS computational biology · 2025Article
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- Medium-sized protein language models perform well at transfer learning on realistic datasets.Scientific reports · 2025Article
- Locality-aware pooling enhances protein language model performance across varied applications.Bioinformatics (Oxford, England) · 2025Article
- Deep learning models for unbiased sequence-based PPI prediction plateau at an accuracy of 0.65.Bioinformatics (Oxford, England) · 2025Article
- From Mechanistic Interpretability to Mechanistic Biology: Training, Evaluating, and Interpreting Sparse Autoencoders on Protein Language Models.bioRxiv : the preprint server for biology · 2025Article
- Pool PaRTI: a PageRank-based pooling method for identifying critical residues and enhancing protein sequence representations.Bioinformatics (Oxford, England) · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.bioRxiv : the preprint server for biology · 2025Article
- Pool PaRTI: A PageRank-Based Pooling Method for Identifying Critical Residues and Enhancing Protein Sequence Representations.bioRxiv : the preprint server for biology · 2025Article
- Democratizing protein language models with parameter-efficient fine-tuning.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Protein language models (PLMs) have emerged as powerful approaches for mapping protein sequences into embeddings suitable for various applications. As protein representation schemes, PLMs generate per-token (i.e. per-residue) representations, resulting in variable-sized outputs based on protein length. This variability poses a challenge for protein-level prediction tasks that require uniform-sized embeddings for consistent analysis across different proteins. Previous work has typically used average pooling to summarize token-level PLM outputs, but it is unclear whether this method effectively prioritizes the relevant information across token-level representations. Results: We introduce a novel method utilizing optimal transport to convert variable-length PLM outputs into fixed-length representations. We conceptualize per-token PLM outputs as samples from a probabilistic distribution and employ sliced-Wasserstein distances to map these samples against a reference set, creating a Euclidean embedding in the output space. The resulting embedding is agnostic to the length of the input and represents the entire protein. We demonstrate the superiority of our method over average pooling for several downstream prediction tasks, particularly with constrained PLM sizes, enabling smaller-scale PLMs to match or exceed the performance of average-pooled larger-scale PLMs. Our aggregation scheme is especially effective for longer protein sequences by capturing essential information that might be lost through average pooling. Availability and implementation: Our implementation code can be found at https://github.com/navid-naderi/PLM_SWE.
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