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

2025-04-25

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<title>Abstract</title> <p>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 m...

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Literature Corpus work: 65d30a38-00b4-5862-ab12-912a48608f26
DOI: 10.21203/rs.3.rs-5926885/v1

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