Yuansong Zeng

CellFM is a large-scale foundation model pre-trained on 100 million human cells, outperforming existing models in cell annotation, perturbation prediction, and gene function prediction.

GraphBepi predicts B-cell epitopes using AlphaFold2 structures and a pretrained language model, outperforming existing methods in AUC and AUPR.

MeHi-SCC improves single-cell RNA-seq clustering by integrating meta-learning and multi-dataset information to identify cell subtypes in large-scale atlases.

This paper introduces ConGI, a method that integrates gene expression and histopathological images via contrastive learning to improve spatial domain identification in spatial transcriptomics.

Hist2ST predicts RNA-seq expression from histology images using a deep learning model combining Transformer and graph neural networks, outperforming existing methods in gene expression prediction and spatial region identification.

ADClust is a parameter-free deep embedding clustering method for scRNA-seq data that automatically determines the number of clusters and outperforms existing methods.

This paper introduces GraphCS, a scalable GNN method for single-cell classification, showing improved performance on large datasets.

scAdapt improves single-cell RNA-seq classification across platforms and species by aligning domain distributions using adversarial learning.

GraphSCC improves single-cell clustering by incorporating structural relations between cells using graph convolutional networks and a dual self-supervised module.