Searching journal content for articles similar to Dong et al. 35 (9): 2052.

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Method: Early feature extraction drives model performance in high-resolution chromatin accessibility prediction
- Aayush Grover,
- Till Muser,
- Liine Kasak,
- Lin Zhang,
- Ekaterina Krymova,
- and Valentina Boeva
Genome Res. March 2026 36: 619-629; Published in Advance January 12, 2026, doi:10.1101/gr.281042.125
..., or attention mechanisms, drive performance in these high-resolution predictions. To address these knowledge gaps, we systematically evaluate classic architectural choices and introduce ConvNeXt V2 blocks, originally developed for computer vision, as high-resolution feature extractors in deep learning models...
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Method: Unified integration of spatial transcriptomics across platforms with LLOKI
- Ellie Haber,
- Ajinkya Deshpande,
- Jian Ma,
- and Spencer Krieger
Genome Res. December 2025 35: 2722-2733; Published in Advance November 13, 2025, doi:10.1101/gr.280803.125
..., Pittsburgh, Pennsylvania 15213, USA Corresponding authors: jianma@cs.cmu.edu, skrieger@andrew.cmu.eduAbstractSpatial transcriptomics (ST) has transformed our understanding of tissue architecture and cellular interactions, but integrating ST data across platforms remains challenging due to differences in gene...
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Method: spRefine denoises and imputes spatial transcriptomics with a reference-free framework powered by genomic language model
- Tianyu Liu,
- Tinglin Huang,
- Wengong Jin,
- Tinyi Chu,
- Rex Ying,
- and Hongyu Zhao
Genome Res. February 3, 2026 gr.281001.125; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
...Refine Clustering Profiles Gene Embeddings Cell Embeddings Genes Fig. 4 Analyzing the results of spRefine pre-trained with large-scale spatial transcriptomics. (A) The workflow of pre-training spRefine for phenotype-level identification. (B) The UMAPs for visually comparing spot representations before (right...
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Method: Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
- Hongyu Zheng,
- Hirak Sarkar,
- and Benjamin J. Raphael
Genome Res. December 2025 35: 2734-2743; Published in Advance November 17, 2025, doi:10.1101/gr.280555.125
....g., combining information from whole-transcriptome platforms with lower spatial resolution with platforms that measure expression of a limited number of genes at high spatial resolution). Such integration could assist in two tasks: (1) predicting the expression of genes that are missing in the high resolution...
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Method: Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover
- An Wang,
- Stephanie Hicks,
- Donald Geman,
- and Laurent Younes
Genome Res. December 2025 35: 2744-2755; Published in Advance November 12, 2025, doi:10.1101/gr.280539.125
...a high-resolution, whole-transcriptome spatial mapping of multiple brain regions. The scFFPE breast cancer data set (Janesick et al. 2023) provides a detailed molecular characterization of breast cancer, identifying 15 distinct cell types to improve understanding of tumor progression and immune...
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Method: STCC enhances spatial domain detection through consensus clustering of spatial transcriptomics data
- Congcong Hu,
- Nana Wei,
- Jiyuan Yang,
- Hua-Jun Wu,
- and Xiaoqi Zheng
Genome Res. June 2025 35: 1415-1428; Published in Advance May 12, 2025, doi:10.1101/gr.280031.124
..., Peking University, Beijing 100191, China; 5Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China Corresponding authors: hjwu@pku.edu.cn, xqzheng@shsmu.edu.cnAbstractThe rapid advance of spatially resolved transcriptomics...
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Method: Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains
- Hang Zhang,
- Yi Zhang,
- Kai Ming Ting,
- Jie Zhang,
- and Qiuran Zhao
Genome Res. February 2025 35: 355-367; Published in Advance February 5, 2025, doi:10.1101/gr.278983.124
...to this work. Corresponding authors: tingkm@nju.edu.cn, zhangj_ai@nju.edu.cnAbstractSpatial transcriptomics are a collection of technologies that have enabled characterization of gene expression profiles and spatial information in tissue samples. Existing methods for clustering spatial transcriptomics data...
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Method: Dissecting multilayer cell–cell communications with signaling feedback loops from spatial transcriptomics data
- Lulu Yan,
- Jinyu Cheng,
- Qing Nie,
- and Xiaoqiang Sun
Genome Res. June 2025 35: 1400-1414; Published in Advance April 22, 2025, doi:10.1101/gr.279857.124
...Dissecting multilayer cell–cell communications with signaling feedback loops from spatial transcriptomics data Lulu Yan1,4, Jinyu Cheng2,4, Qing Nie3 and Xiaoqiang Sun1 1School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China; 2Zhongshan School of Medicine, Sun Yat-sen University...
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Method: Deciphering context-specific gene programs from single-cell and spatial transcriptomics data with DeCEP
- Lin Li,
- Xianbin Su,
- and Ze-Guang Han
Genome Res. October 2025 35: 2300-2315; Published in Advance August 21, 2025, doi:10.1101/gr.279689.124
..., the development of spatial transcriptomics (ST) technologies has enabled the depiction of spatial coregulation patterns of gene programs within a specific tissue (Rao et al. 2021; Moses and Pachter 2022; Walker et al. 2022). However, current computational methods for systematically deciphering gene programs...
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Method: Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics
- Vishvak Raghavan,
- Yumin Zheng,
- Yue Li,
- and Jun Ding
Genome Res. July 2025 35: 1646-1663; Published in Advance May 2, 2025, doi:10.1101/gr.279771.124
...Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics Vishvak Raghavan1,2,3, Yumin Zheng2, Yue Li1,3 and Jun Ding1,2,3 1School of Computer Science, McGill University, Montreal, Quebec H3A 2A7, Canada; 2Meakins...
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