Pool PaRTI: A PageRank-Based Pooling Method for Identifying Critical Residues and Enhancing Protein Sequence Representations.

ABSTRACT:

Motivation

Protein language models produce token-level embeddings for each residue, resulting in an output matrix with dimensions that vary based on sequence length. However, downstream machine learning models typically require fixed-length input vectors, necessitating a pooling method to compress the output matrix into a single vector representation of the entire protein. Traditional pooling methods often result in substantial information loss, impacting downstream task performance. We aim to develop a pooling method that produces more expressive general-purpose protein embedding vectors while offering biological interpretability.

Results

We introduce Pool PaRTI, a novel pooling method that leverages internal transformer attention matrices and PageRank to assign token impor

SUBMITTER: Tartici A 

PROVIDER: S-EPMC11956911 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature