Chunman Zuo

Vector-guided graph learning for spatial multi-slice multi-omics alignment

Xuan Li, Qixing Yang, Hao Dai, Kewei Ma et autres

Spatial mapping of multi-slice multi-omics data enables the identification of shared and slice-specific cellular components across spatiotemporal axes. However, conventional graph neural networks assume uniform contributions from neighboring cells, neglecting directional and angular influences that shape central cell states and limiting their …

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NeoAtlas-Tumor and NeoBert: A Database and A Predictive Model for Canonical and Noncanonical Tumor Neoantigens

Meilong Shi, Qianyi Yan, Wei Zhao, Chuanqi Teng et autres

Neoantigens are classified into canonical and noncanonical types. Noncanonical neoantigens include those derived from noncoding regions, transposable elements (TE), and intron retention events, and they have recently gained considerable attention in cancer immunity. In this study, we focused on neoantigens presented by …

cn (code pays fourni par la source)

Spatiotemporal multi-omics analysis uncovers NAD-dependent immunosuppressive niche triggering early gastric cancer

Pingting Gao, Chunman Zuo, Wei Yuan, Jiabin Cai et autres

Abstract Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies. By characterizing key molecular, cellular and niche events at the precancerous tipping point of early gastric cancer (EGC), …

cn, Éthiopie (code pays fourni par la source)

stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning

Yupeng Xu, Hao Dai, Jinwang Feng, Keren Xu et autres

The growing availability of spatial transcriptomics data offers key resources for annotating query datasets using reference datasets. However, batch effects, unbalanced reference annotations, and tissue heterogeneity pose significant challenges to alignment analysis. Here, we present stGuide, an attention-based supervised graph learning model …

cn (code pays fourni par la source)

Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data

Chunman Zuo, Junchao Zhu, Jiawei Zou, Luonan Chen

Analysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi-omics technologies now reveal complex molecular interactions shaping cellular behaviour and tissue dynamics. This review highlights key …

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Advanced Cross-Graph Cycle Attention Model for Dissecting Complex Structures in Mass Spectrometry Imaging

Jiangnan Cui, Yang Gao, Qian Wang, Xuan Li et autres

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NeoAtlas and NeoBert: A Database and A Predictive Model for Canonical and Noncanonical Tumor Neoantigens

Meilong Shi, Qianyi Yan, Wei Zhao, Chuanqi Teng et autres

Abstract Neoantigens are classified into canonical and noncanonical types. Noncanonical neoantigens include those derived from noncoding regions, transposable elements (TE), and intron retention events, and they have recently gained considerable attention in cancer immunity. We curated 35,574 non-redundant neoantigen-HLA pairs from 14 …

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Supervised contrastive knowledge graph learning for ncRNA–disease association prediction

Yan Wang, Xuping Xie, Nan Sheng, Lan Huang et autres

cn (code pays fourni par la source)

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2025

Yīmíng Bào, Xue Lian Bai, Congfan Bu, Haobin Chen et autres

The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), offers a comprehensive suite of database resources to support the global scientific community. Amidst the unprecedented accumulation of multi-omics data, CNCB-NGDC is committed to …

GNNMF: a multi-view graph neural network for ATAC-seq motif finding

Shuangquan Zhang, Xiaotian Wu, Zhichao Lian, Chunman Zuo et autres

BACKGROUND: The Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) utilizes the Transposase Tn5 to probe open chromatic, which simultaneously reveals multiple transcription factor binding sites (TFBSs) compared to traditional technologies. Deep learning (DL) technology, including convolutional neural networks (CNNs), has successfully found …

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RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder

Jie Xia, Lequn Wang, Guijun Zhang, Chunman Zuo et autres

Rapid advances in single-cell genomics sequencing (SCGS) have allowed researchers to characterize tumor heterozygosity with unprecedented resolution and reveal the phylogenetic relationships between tumor cells or clones. However, high sequencing error rates of current SCGS data, i.e., false positives, false negatives, and …

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Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data

Chunman Zuo, Hao Dai, Luonan Chen

MOTIVATION: Joint profiling of single-cell transcriptomics and epigenomics data enables us to characterize cell states and transcriptomics regulatory programs related to cellular heterogeneity. However, the highly different features on sparsity, heterogeneity and dimensionality between multi-omics data have severely hindered its integrative analysis. …

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