Reprogramming pretrained language models for protein sequence representation learning

The representation reprogramming via dictionary learning framework reprograms a pretrained English language model to learn the embeddings of protein sequences by learning a sparse linear mapping between English and protein vocabulary embeddings.

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Publication Details

Journal
Digital Discovery
Published
2025-01-01
DOI
https://doi.org/10.1039/d4dd00195h
Citations
1
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.81
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article

Reprogramming pretrained language models for protein sequence representation learning

Ria Vinod, Pin‐Yu Chen, Payel Das
1 citations
Digital Discovery
Machine Learning in Bioinformatics
0.81
article

Reprogramming pretrained language models for protein sequence representation learning

Ria Vinod, Pin‐Yu Chen, Payel Das
article en
1 citations

Abstract

The representation reprogramming via dictionary learning framework reprograms a pretrained English language model to learn the embeddings of protein sequences by learning a sparse linear mapping between English and protein vocabulary embeddings.

Digital DiscoveryVol. 4(6)
John Brown University (US)
Openalex Percentile: Top 28%
Machine Learning in Bioinformatics
0.81
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Reprogramming pretrained language models for protein sequence representation learning — Ria Vinod, Pin‐Yu Chen, et al. · Digital Discovery (2025) | TGRS Research Map | TGRS