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Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification
Fei He1, Ruixin Fei2, Mingyue Gao2
1Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
Parameter-Efficient Fine-Tuning (PEFT) improves single-cell Large Language Models (scLLMs) for cell type identification. This method enhances performance while significantly reducing computational costs compared to traditional fine-tuning.
Area of Science:
- Computational Biology
- Genomics
- Artificial Intelligence
Background:
- Single-cell sequencing provides high-resolution cellular data but suffers from high variability.
- Single-cell Large Language Models (scLLMs) aim to identify universal patterns in this data.
- Existing scLLMs struggle in zero-shot settings, necessitating better adaptation techniques.
Conclusions:
- PEFT offers an efficient and effective approach for adapting scLLMs.
- This methodology overcomes key challenges associated with traditional fine-tuning.
- Paves the way for broader and more efficient application of scLLMs in single-cell biology.
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