Article

Harnessing the Power of Single-Cell Large Language Models with Parameter Efficient Fine-Tuning using scPEFT

2025-04-23

Abstract excerpt

Single-cell large language models (scLLMs) capture essential biological insights from vast single-cell atlases but struggle in out-of-context applications, where zero-shot predictions can be unreliable. To address this, we introduce a single-cell parameter-efficient fine-tuning (scPEFT) framework that integrates learnable, low-dimensional adapters into scLLMs. By freezing the backbone model and updating only the a...

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Literature Corpus work: c1e88c04-5783-5105-b1ee-3545e313cd4e
DOI: 10.1101/2025.04.21.649754

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