Fei He

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

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.

Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type Identification

This paper introduces parameter-efficient fine-tuning strategies to improve cell type identification in single-cell models, achieving better performance with reduced computational cost.

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning

RESEPT uses deep learning to visualize and characterize tissue architecture from spatial transcriptomics data, revealing pathological structures in Alzheimer's and glioblastoma samples.

TMPs Interaction Domain: A Discovery of Structural Universality Among TMPs Interaction Sites Using Deep Learning Method

The paper uses a 3D deep learning model to find structural universality in TMPs interaction regions, achieving an MCC of 0.36.