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Method: A model-based constrained deep learning clustering approach for spatially resolved single-cell data
- Xiang Lin,
- Le Gao,
- Nathan Whitener,
- Ashley Ahmed,
- and Zhi Wei
Genome Res. October 2022 32: 1906-1917; Published in Advance October 5, 2022, doi:10.1101/gr.276477.121
..., the information from the markers can be used as prior knowledge to guide the sp-scRNA-seq analyses, especially for the clustering analysis. However, none of the methods mentioned above can incorporate the marker gene information in the clustering process.In this article, we propose a novel clustering approach...
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Review: Artificial intelligence and machine learning in cell-free-DNA-based diagnostics
- W.H. Adrian Tsui,
- Spencer C. Ding,
- Peiyong Jiang,
- and Y.M. Dennis Lo
Genome Res. January 2025 35: 1-19; doi:10.1101/gr.278413.123
...truly intelligent behavior (Wooldridge 2021). The explosive increase of computational power in the twenty-first century enabled deep learning, which uses “deep” neural networks (DNNs), to be applied widely (Hoffmann 2022). Because of the analogies to neuronal mechanics in the brain, deep learning is now...
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Method: A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq data
- Mengyuan Zhao,
- Jiawei Li,
- Xiaoyi Liu,
- Ke Ma,
- Jijun Tang,
- and Fei Guo
Genome Res. July 2024 34: 1036-1051; Published in Advance August 12, 2024, doi:10.1101/gr.278439.123
...and biological heterogeneity.To address these issues, the typical approach is dividing extensive cells into biologically meaningful clusters according to expression patterns. Then, specific types are assigned to each cluster using prior knowledge, such as marker genes (Lopez et al. 2018; Pliner et al. 2019...
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Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
- Yidi Deng,
- Jiadong Mao,
- Jarny Choi,
- and Kim-Anh Lê Cao
Genome Res. March 5, 2026 ; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
...-specific networks into meta-networks that capture primary coexpression patterns and (ii) integrating prior knowledge to annotate coexpression and infer active signaling interactions at the individual cell level. All functional modules of NNet are implemented with an efficient algorithm that enables the application...
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Method: spRefine denoises and imputes spatial transcriptomic data 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 ; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
...states of patients from The Cancer Genome Atlas (TCGA) database (Weinstein et al. 2013). Our results show the strong capacity of spRefine in transferring the learned knowledge to biomedical data with different resolutions and improving our understanding of aging and cancer processes. Details of model...
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Method: Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN
- Wenhao Zhang,
- Lan Cao,
- Xiaoxuan Gu,
- Yongyu Long,
- and Ying Wang
Genome Res. January 2026 36: 142-158; Published in Advance October 9, 2025, doi:10.1101/gr.280757.125
..., reconstructing cell type–specific networks and allowing in silico T perturbations to reveal dynamic regulatory mechanisms. The knowledge-primed model SupirFactor (Tjärnberg et al. 2024) embeds the prior GRN directly into deep learning architectures, enabling biologically interpretable GRN predictions while...
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Review: A systematic guide for identifying transcription factors that directly regulate the expression of a gene of interest
- Andrew D. Bates,
- Dawid Grzela,
- Maciej Studzian,
- Louise Brennan,
- Moli Williams,
- Conor Fawcett,
- Beth Hammond,
- Manreen Grewal,
- Marcin Ratajewski,
- Lukasz Pulaski,
- and Urszula L. McClurg
Genome Res. March 2026 36: 433-459; Published in Advance February 17, 2026, doi:10.1101/gr.281154.125
...), and TRIPOD incorporate chromatin data, enhancing specificity. TFBShape and CRPTS account for DNA shape influences (Yang et al. 2014). Given the inherent challenges, modern prediction tools often incorporate machine learning to enhance traditional methods. Early tools like hidden Markov models and support...
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Method: A scalable computational framework for predicting gene expression from candidate cis-regulatory elements
- Qinhu Zhang,
- Siguo Wang,
- Zhipeng Li,
- Wenzheng Bao,
- Wenjian Liu,
- and De-Shuang Huang
Genome Res. February 2026 36: 361-374; Published in Advance January 16, 2026, doi:10.1101/gr.281219.125
...and transformer to predict gene expression, where CNN is used to learn sequence and chromatin features and transformer is used to learn the relationships between genes and cCREs. Meanwhile, we designed two ways to improve the performance of ScPGE by incorporating chromatin loops. In the stage of model...
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Method: High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND
- Kehan Lang,
- Chenyang Jia,
- Siyu Li,
- Yi Guo,
- Dingjun Hu,
- and Shengquan Chen
Genome Res. March 26, 2026 ; Published in Advance March 26, 2026, doi:10.1101/gr.281350.125
...conditions warrant further investigation. The following are some possible improvementmethods. First, integrating prior knowledge from bulk omic atlases or curated gene regulatory networks could improve the biological plausibility and precision of cross-modality mappings (Chen et al. 2021; Cui et al. 2024...
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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
...data as user input (Fig. 1A). It infers CCC by integrating this input data with prior knowledge of the interactions between signaling ligands, receptors, transcription factors (TFs), and TGs. Upon receiving input data, stMLnet models intercellular and intracellular communications through the following...

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