Author

Huazhu Fu

Agency for Science, Technology and ResearchORCID 0000-0002-9702-5524Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Neuroscience· 1

Signature Assays, Organisms & Methods

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Research Focus:clustering· 3 Organism:human· 3 Research Focus:batch correction / integration· 2 Approach:deep learning· 2 Assay:spatial transcriptomics· 2 Approach:contrastive learning· 1 Research Focus:deconvolution· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Ao CheniD· 2 shared Hang XuiD· 2 shared Jinmiao CheniD· 2 shared Kok Siong AngiD· 2 shared Longqi LiuiD· 2 shared Mengwei LiiD· 2 shared Alzheimer’s Disease Neuroimaging Initiative· 1 shared Chengwei Zhong· 1 shared

Activity by Year

2021-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersBioinformatics

Ao ChenORCID

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Author profile →

3 papers

Recent·Most cited

WMH-DualTasker: A weakly-supervised deep learning model for automated white matter hyperintensities segmentation and visual rating prediction

Yilei Wu, Zijian Dong, Hongwei Li, Yao Feng Victor Chong, Fang Ji, Joanna Su Xian Chong, Nathanael Ren Jie Tong, Saima Hilal, Huazhu Fu, Christopher Chen, Juan Helen Zhou, Alzheimer’s Disease Neuroimaging Initiative

WMH-DualTasker is a weakly-supervised deep learning model that segments white matter hyperintensities and predicts visual ratings, outperforming supervised methods on benchmark datasets.

Human Brain Mapping · Apr 15, 2025Peer-reviewedNeuroscience

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

Yahui Long, Kok Siong Ang, Mengwei Li, Kian Long Kelvin Chong, Raman Sethi, Chengwei Zhong, Hang Xu, Zhiwei Ong, Karishma Sachaphibulkij, Ao Chen, Li Zeng, Huazhu Fu, Min Wu, Lina H. K. Lim, Longqi Liu, Jinmiao Chen

GraphST improves spatial transcriptomics analysis by integrating clustering, integration, and deconvolution through graph self-supervised contrastive learning, achieving higher accuracy than existing methods.

Nature Communications · Mar 1, 2023Peer-reviewedBioinformatics

Unsupervised Spatial Embedded Deep Representation of Spatial Transcriptomics

Huazhu Fu, Hang Xu, Kelvin Chong, Mengwei Li, Kok Siong Ang, Hong Kai Lee, Jingjing Ling, Ao Chen, Ling Shao, Longqi Liu, Jinmiao Chen

SEDR couples a deep autoencoder over expression with a variational graph autoencoder over position, improving clustering in dorsolateral prefrontal cortex and splitting a visually uniform breast tumour region.

bioRxiv · Jun 15, 2021 · PreprintBioinformatics