Yibo Chen

This paper introduces scPEFT, a parameter-efficient fine-tuning framework for scLLMs that improves task adaptation with reduced computational costs and enhances performance in disease-specific and cross-species analyses.

This study improves gene relationship prediction using iterative prompt refinement with ChatGPT, achieving better performance for complex gene interactions.

The paper introduces pathCLIP, a system that identifies genes and gene relations from biological pathway figures using image-text contrastive learning, outperforming single-modality approaches.

This paper evaluates 21 large language models for mining gene interactions and pathway knowledge, finding significant performance differences between API-based and open-source models.