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Spatially aligned graph transfer learning for characterizing spatial regulatory heterogeneity.

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第一作者： Wendong,Huang

第一单位： Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.;Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

作者： Wendong,Huang ^[1] ; Yaofeng,Hu ^[2] ; Lequn,Wang ^[3] ; Guangsheng,Wu ^[4] ; Chuanchao,Zhang ^[2] ; Qianqian,Shi ^[1]

作者单位： Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.;Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China. ^[1] Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China. ^[2] State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China. ^[3] School of Mathematics and Computer Science, Xinyu University, Xinyu 338004, Jiangxi, China. ^[4]

关键词 cross-dimensional transfer learning graph transformers spatial regulatory network inference spatially resolved transcriptomics

主题词算法(Algorithms);人类(Humans);基因调控网络(Gene Regulatory Networks);计算生物学(Computational Biology);基因表达谱(Gene Expression Profiling)

DOI 10.1093/bib/bbaf021

PMID 39841593

发布时间 2025-05-02

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