Author

Weijiang Yu

Sun Yat-sen UniversityORCID 0000-0002-7449-3093Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:clustering· 2 Research Focus:gene regulatory network inference· 2 Approach:graph neural network· 2 Organism:human· 2 Assay:imaging· 2 Approach:machine learning (classical)· 2 Assay:scRNA-seq· 2 Assay:spatial transcriptomics· 2

Frequent Co-Authors

Yuansong ZengiD· 5 shared Yuedong YangiD· 5 shared Yutong LuiD· 4 shared Zhuoyi Wei· 3 shared Rui YiniD· 2 shared Zixiang PaniD· 2 shared Bingling LiiD· 1 shared Chengyang ZhangiD· 1 shared

Activity by Year

2022-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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5 shared papersBioinformatics

Yuansong ZengORCID

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Author profile →

5 papers

Recent·Most cited

CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells

Yuansong Zeng, Jiancong Xie, Ningyuan Shangguan, Zhuoyi Wei, Wenbing Li, Yun Su, Shuangyu Yang, Chengyang Zhang, Jinbo Zhang, Nan Fang, Hongyu Zhang, Yutong Lu, Huiying Zhao, Jue Fan, Weijiang Yu, Yuedong Yang

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.

Nature Communications · May 20, 2025Peer-reviewedBioinformatics

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model

Yuansong Zeng, Zhuoyi Wei, Qianmu Yuan, Sheng Chen, Weijiang Yu, Yutong Lu, Jianzhao Gao, Yuedong Yang

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

Bioinformatics · Apr 1, 2023Peer-reviewedBioinformatics

A Meta-learning based Graph-Hierarchical Clustering Method for Single Cell RNA-Seq Data

Zixiang Pan, Yuefan Lin, Haokun Zhang, Yuansong Zeng, Weijiang Yu, Yuedong Yang

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

IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · Dec 6, 2022Peer-reviewedBioinformatics

Deciphering Spatial Domains by Integrating Histopathological Image and Transcriptomics via Contrastive Learning

Yuansong Zeng, Rui Yin, Mai Luo, Jianing Chen, Zixiang Pan, Yutong Lu, Weijiang Yu, Yuedong Yang

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

bioRxiv · Sep 30, 2022 · PreprintBioinformatics

Spatial Transcriptomics Prediction from Histology jointly through Transformer and Graph Neural Networks

Yuansong Zeng, Zhuoyi Wei, Weijiang Yu, Rui Yin, Yuchen Yuan, Bingling Li, Zhonghui Tang, Yutong Lu, Yuedong Yang

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.

Briefings in Bioinformatics · Jul 17, 2022Peer-reviewedBioinformatics