Parameter-efficient fine-tuning enables scalable transfer of regulatory sequence models to novel contexts

Our framework offers a scalable and practical solution for extending large sequence models to novel biological contexts, enabling mechanistic insight into gene regulation and variant effects.

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Publication Details

Journal
Genome biology
Published
2026-01-30
DOI
https://doi.org/10.1186/s13059-026-03935-0
Primary Topic
Genomics and Chromatin Dynamics
Type
article
Field-Weighted Citation Impact
0.00
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article

Parameter-efficient fine-tuning enables scalable transfer of regulatory sequence models to novel contexts

Johannes Linder, Han Yuan, David R. Kelley
Genome biology
Genomics and Chromatin Dynamics
article

Parameter-efficient fine-tuning enables scalable transfer of regulatory sequence models to novel contexts

Johannes Linder, Han Yuan, David R. Kelley
article en

Abstract

Our framework offers a scalable and practical solution for extending large sequence models to novel biological contexts, enabling mechanistic insight into gene regulation and variant effects.

Genome biologyVol. 27(1)
ISCO International (United States) (US)
Openalex Percentile: Top 73%
Genomics and Chromatin Dynamics
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Parameter-efficient fine-tuning enables scalable transfer of regulatory sequence models to novel contexts — Johannes Linder, Han Yuan, et al. · Genome biology (2026) | TGRS Research Map | TGRS