Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors
Open accessAbstract
Recent developments in spatial transcriptomics (ST) technologies have enabled the profiling of transcriptome-wide gene expression while retaining the location information of measured genes within tissues. Moreover, the corresponding high-resolution hematoxylin and eosin-stained histology images are readily available for the ST tissue sections. Since histology images are easy to obtain, it is desirable to leverage information learned from ST to predict gene expression for tissue sections where only histology images are available. Here we present HisToGene, a deep learning model for gene expression prediction from histology images. To account for the spatial dependency of measured spots, HisToGene adopts Vision Transformer, a state-of-the-art method for image recognition. The well-trained HisToGene model can also predict super-resolution gene expression. Through evaluations on 32 HER2+ breast cancer samples with 9,612 spots and 785 genes, we show that HisToGene accurately predicts gene expression and outperforms ST-Net both in gene expression prediction and clustering tissue regions using the predicted expression. We further show that the predicted super-resolution gene expression also leads to higher clustering accuracy than observed gene expression. Gene expression predicted from HisToGene enables researchers to generate virtual transcriptomics data at scale and can help elucidate the molecular signatures of tissues.
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Several studies have explored predicting gene expression from H&E images using AI models.
“Several studies have explored predicting gene expression from H&E images using AI models59–62.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [59]
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The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807 · see citing works
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021) · arXiv · doi:10.48550/arxiv.2102.04306 · Cited by 4,206 · cited 1× · see citing works
“However, this bias has been recently alleviated by Vision Transformer (17), which internally utilizes self-attention mechanism for divided image patches and has shown strong performance on many tasks, including medical image classification, segmentation (18), and registration (19).”
BackgroundVisualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · see citing works
Fast gene set enrichment analysisPreprint
Korotkevich, G., Sukhov, V., Budin, N., Shpak, B., Artyomov, M. N., & Sergushichev, A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/060012 · Cited by 3,742 · see citing works
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424 · see citing works
Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
Stickels, R. R., Murray, E., Kumar, P., Li, J., Marshall, J. L., Di Bella, D. J., Arlotta, P., Macosko, E. Z., & Chen, F. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0739-1 · Cited by 1,295 · see citing works
High-definition spatial transcriptomics for in situ tissue profiling
Vickovic, S., Eraslan, G., Salmén, F., Klughammer, J., Stenbeck, L., Schapiro, D., Äijö, T., Bonneau, R., Bergenstråhle, L., Navarro, J. F., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0548-y · Cited by 1,150 · see citing works
Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.039 · Cited by 1,117 · cited 1× · see citing works
“cutaneous squamous cell carcinoma (cSCC) dataset (37), which includes 12 tissue sections 421 obtained from 4 patients, with each patient having 3 sections.”
BackgroundHu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D. J., Lee, E. B., Shinohara, R. T., & Li, M. (2021) · Nature Methods · doi:10.1038/s41592-021-01255-8 · Cited by 1,113 · see citing works
Self-citationHigh-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue
Liu, Y., Yang, M., Deng, Y., Su, G., Enninful, A., Guo, C. C., Tebaldi, T., Zhang, D., Kim, D., Bai, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.026 · Cited by 966 · see citing works
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949 · cited 1× · see citing works
“Applications of the ST technologies in diverse tissues and 39 diseases have transformed our views of transcriptome complexity (7-9).”
BackgroundA Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart
Asp, M., Giacomello, S., Larsson, L., Wu, C., Fürth, D., Qian, X., Wärdell, E., Custodio, J., Reimegård, J., Salmén, F., et al. (2019) · Cell · doi:10.1016/j.cell.2019.11.025 · Cited by 740 · see citing works
Spatially resolved transcriptomes—next generation tools for tissue exploration
Asp, M., Bergenstråhle, J., & Lundeberg, J. (2020) · BioEssays · doi:10.1002/bies.201900221 · Cited by 666 · see citing works
Fatty Acid Synthase As a Potential Therapeutic Target in Cancer
Flavin, R., Peluso, S., Nguyen, P. L., & Loda, M. F. (2010) · Future Oncology · doi:10.2217/fon.10.11 · Cited by 545 · see citing works
A deep learning model to predict RNA-Seq expression of tumours from whole-slide images
Schmauch, B., Romagnoni, A., Pronier, E., Saillard, C., Maillé, P., Calderaro, J., Kamoun, A., Sefta, M., Toldo, S., Zaslavskiy, M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17678-4 · Cited by 533 · see citing works
Spatially resolved transcriptomics and beyond
Crosetto, N., Bienko, M., & van Oudenaarden, A. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3832 · Cited by 509 · see citing works
Integrating spatial gene expression and breast tumour morphology via deep learning
He, B., Bergenstråhle, L., Stenbeck, L., Abid, A., Andersson, A., Borg, Å., Maaskola, J., Lundeberg, J., & Zou, J. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-0578-x · Cited by 507 · see citing works
Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies
Sun, S., Zhu, J., & Zhou, X. (2020) · Nature Methods · doi:10.1038/s41592-019-0701-7 · Cited by 502 · see citing works
Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis
Maniatis, S., Äijö, T., Vickovic, S., Braine, C., Kang, K., Mollbrink, A., Fagegaltier, D., Andrusivová, Ž., Saarenpää, S., Saiz-Castro, G., et al. (2019) · Science · doi:10.1126/science.aav9776 · Cited by 429 · see citing works
Andersson, A., Larsson, L., Stenbeck, L., Salmén, F., Ehinger, A., Wu, S. Z., Al-Eryani, G., Roden, D., Swarbrick, A., Borg, Å., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26271-2 · Cited by 404 · see citing works
Adaptation of Energy Metabolism in Breast Cancer Brain Metastases
Chen, E. I., Hewel, J. A., Krueger, J. S., Tiraby, C., Weber, M. R., Kralli, A., Becker, K., Yates, J. R., & Felding‐Habermann, B. (2007) · Cancer Research · doi:10.1158/0008-5472.can-06-3137 · Cited by 353 · see citing works
Spatial transcriptomics coming of age
Burgess, D. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0129-z · Cited by 323 · see citing works
Fatty acid synthase (FASN) as a therapeutic target in breast cancer
Menéndez, J. A., & Lupu, R. (2017) · Expert Opinion on Therapeutic Targets · doi:10.1080/14728222.2017.1381087 · Cited by 293 · see citing works
Liao, J., Lu, X., Shao, X., Zhu, L., & Fan, X. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.05.006 · Cited by 278 · see citing works
Georgouli, M., Herráiz, C., Crosas‐Molist, E., Fanshawe, B., Maiques, Ó., Perdrix-Rosell, A., Pandya, P., Rodríguez‐Hernández, I., Ilieva, K. M., Cantelli, G., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.038 · Cited by 170 · see citing works
Spatial transcriptomics: paving the way for tissue-level systems biology
Moor, A. E., & Itzkovitz, S. (2017) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2017.02.004 · Cited by 158 · see citing works
Lanigan, F. T., McKiernan, E., Brennan, D. J., Hegarty, S. M., Millikan, R. C., McBryan, J., Jirström, K., Landberg, G., Martin, F., Duffy, M. J., et al. (2008) · International Journal of Cancer · doi:10.1002/ijc.24159 · Cited by 152 · see citing works
From whole-mount to single-cell spatial assessment of gene expression in 3D
Waylen, L. N., Nim, H. T., Martelotto, L. G., & Ramialison, M. (2020) · Communications Biology · doi:10.1038/s42003-020-01341-1 · Cited by 138 · see citing works
ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image RegistrationPreprint
Chen, J., He, Y., Frey, E. C., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06468 · Cited by 138 · see citing works
Statistical and machine learning methods for spatially resolved transcriptomics with histology
Hu, J., Schroeder, A. R., Coleman, K., Chen, C., Auerbach, B. J., & Li, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.052 · Cited by 81 · see citing works
Self-citationJin, X., Zhu, L., Cui, Z., Tang, J., Xie, M., & Ren, G. (2019) · Clinical & Translational Oncology · doi:10.1007/s12094-019-02042-w · Cited by 70 · see citing works
Upregulation of Claudin-4, CAIX and GLUT-1 in distant breast cancer metastases
Hiemcke‐Jiwa, L. S., van Diest, P. J., Hoefnagel, L. D. C., Wesseling, J., Wesseling, P., & Moelans, C. B. (2014) · BMC Cancer · doi:10.1186/1471-2407-14-864 · Cited by 62 · see citing works
Szász, A. M., Tõkés, A., Micsinai, M., Krenács, T., Jakab, C., Lukacs, L., Németh, Z., Baranyai, Z., Dede, K., Madaras, L., et al. (2010) · Clinical & Experimental Metastasis · doi:10.1007/s10585-010-9357-5 · Cited by 62 · see citing works
Systematic identification of the key candidate genes in breast cancer stroma
Wang, Y., Xu, H., Zhu, B., Qiu, Z., & Lin, Z. (2018) · Cellular & Molecular Biology Letters · doi:10.1186/s11658-018-0110-4 · Cited by 50 · see citing works
Thyroid hormones and breast cancer association according to menopausal status and body mass index
Ortega-Olvera, C., Ulloa-Aguirre, A., Ángeles-Llerenas, A., Mainero-Ratchelous, F., González-Acevedo, C. E., Hernández-Blanco, M. D. L., Ziv, E., Avilés-Santa, L., Pérez-Rodríguez, E., & Torres-Mejía, G. (2018) · Breast Cancer Research · doi:10.1186/s13058-018-1017-8 · Cited by 37 · see citing works
Su, K., Yu, Q., Shen, R., Sun, S., Moreno, C. S., Li, X., & Qin, Z. S. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100050 · Cited by 34 · see citing works
Self-citationHu, J., Li, X., Coleman, K., Schroeder, A. R., Irwin, D. J., Lee, E. B., Shinohara, R. T., & Li, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.30.405118 · Cited by 26 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. et al. (2020) An image is worth 16x16 words: transformer for image recognition at scale. ICLR 2021 Proceedings.
Cited by
Sorted by citations
Zeng, Y., Wei, Z., Yu, W., Yin, R., Yuan, Y., Li, B., Tang, Z., Lu, Y., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac297 · Cited by 211 · see citing works
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
Zeng, Z., Li, Y., Li, Y., & Luo, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02653-7 · Cited by 186 · see citing works
THItoGene: a deep learning method for predicting spatial transcriptomics from histological images
Jia, Y., Liu, J., Chen, L., Zhao, T., & Wang, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad464 · Cited by 130 · see citing works
A visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118 · see citing works
“As an application of Tangram with OmiCLIP embeddings instead of gene expression data, Loki Decompose applied Tangram’s nonconvex optimization algorithm 47 to deconvolute the OmiCLIP embeddings of an H&E image patch or the embeddings of a Visium spot’s transcriptomic profile rather than raw gene…”
Wang, B., Luo, J., Liu, Y., Shi, W., Xiong, Z., Shen, C., & Long, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad262 · Cited by 88 · see citing works
Multimodal contrastive learning for spatial gene expression prediction using histology images
Min, W., Shi, Z., Zhang, J., Wan, J., & Wang, C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae551 · Cited by 90 · see citing works
Single-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 81 · see citing works
Identifying spatial domain by adapting transcriptomics with histology through contrastive learning
Zeng, Y., Yin, R., Luo, M., Chen, J., Pan, Z., Lu, Y., Yu, W., & Yang, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad048 · Cited by 66 · see citing works
Deep learning in spatially resolved transcriptomics: a comprehensive technical view
Zahedi, R., Ghamsari, M. R. E., Argha, A., MacPhillamy, C., Beheshti, A., Alizadehsani, R., Lovell, N. H., Lotfollahi, M., & Alinejad‐Rokny, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae082 · Cited by 74 · see citing works
Combining spatial transcriptomics with tissue morphology
Chelebian, E., Avenel, C., & Wählby, C. (2025) · Nature Communications · doi:10.1038/s41467-025-58989-8 · Cited by 64 · see citing works
Recent advances in spatially resolved transcriptomics: challenges and opportunities
Lee, J., Yoo, M., & Choi, J. (2022) · BMB Reports · doi:10.5483/bmbrep.2022.55.3.014 · Cited by 61 · see citing works
Benchmarking the translational potential of spatial gene expression prediction from histology
Wang, C., Chan, A. S., Fu, X., Ghazanfar, S., Kim, J., Patrick, E., & Yang, J. Y. H. (2025) · Nature Communications · doi:10.1038/s41467-025-56618-y · Cited by 58 · see citing works
Spatial multiplexing and omics
Carstens, J. L., Krishnan, S., Rao, A. U. K., Sorace, A. G., Seeley, E. H., Borgogno, S. F., & Burks, J. K. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00330-6 · Cited by 49 · see citing works
Xiao, X., Kong, Y., Li, R.-H., Wang, Z., & Lu, H. J. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103040 · Cited by 44 · see citing works
Gao, R., Yuan, X., Ma, Y., Wei, T., Johnston, L., Shao, Y., Lv, W., Zhu, T., Zhang, Y., Zheng, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101536 · Cited by 45 · see citing works
Yang, Y., Cui, Y., Zeng, X., Zhang, Y., Loza, M., Park, S., & Nakai, K. (2025) · Nature Communications · doi:10.1038/s41467-025-56276-0 · Cited by 47 · see citing works
Spatial gene expression at single-cell resolution from histology using deep learning with GHIST
Fu, X., Cao, Y., Bian, B., Wang, C., Graham, J. D., Pathmanathan, N., Patrick, E., Kim, J., & Yang, J. Y. H. (2025) · Nature Methods · doi:10.1038/s41592-025-02795-z · Cited by 47 · see citing works
Accurate Spatial Gene Expression Prediction by Integrating Multi-Resolution Features
Chung, Y., Ha, J. H., Im, K. C., & Lee, J. S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01101 · Cited by 42 · see citing works
Gene expression prediction from histology images via hypergraph neural networks
Li, B., Zhang, Y., Wang, Q., Zhang, C., Li, M., Wang, G., & Song, Q. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae500 · Cited by 33 · see citing works
Self-citationSystematic inference of super-resolution cell spatial profiles from histology images
Zhang, P., Gao, C., Zhang, Z., Yuan, Z., Zhang, Q., Zhang, P., Du, S., Zhou, W., Li, Y., & Li, S. (2025) · Nature Communications · doi:10.1038/s41467-025-57072-6 · Cited by 32 · see citing works
Natural Language Processing Methods for the Study of Protein–Ligand Interactions
Michels, J., Bandarupalli, R., Akbari, A. A., Le, T., Xiao, H. Y., Li, J., & Hom, E. F. Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01907 · Cited by 33 · see citing works
Deep topographic proteomics of a human brain tumour
Davis, S. A., Scott, C., Oetjen, J., Charles, P. D., Kessler, B. M., Ansorge, O., & Fischer, R. (2023) · Nature Communications · doi:10.1038/s41467-023-43520-8 · Cited by 30 · see citing works
High-density generation of spatial transcriptomics with STAGE
Li, S., Gai, K., Dong, K., Zhang, Y., & Zhang, S. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae294 · Cited by 31 · see citing works
A deep learning-based multiscale integration of spatial omics with tumor morphology
Schmauch, B., Herpin, L., Olivier, A., Duboudin, T., Dubois, R., Gillet, L., Filiot, A., Schiratti, J.-B., Proietto, V. D., Corre, D. L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66691-y · Cited by 26 · see citing works
DIST: spatial transcriptomics enhancement using deep learning
Zhao, Y., Wang, K., & Hu, G. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad013 · Cited by 22 · see citing works
Fatemi, M., Feng, E., Sharma, C., Azher, Z., Goel, T., Ramwala, O. A., Palisoul, S. M., Barney, R. E., Perreard, L., Kolling, F., et al. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100308 · Cited by 22 · see citing works
Spatial omics techniques and data analysis for cancer immunotherapy applications
Zhang, Y., Lee, R. Y., Tan, C. W., Guo, X., Yim, W. W., Lim, J., Wee, F. Y. T., Wu, Y., Kharbanda, M., Lee, J.-Y. J., et al. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103111 · Cited by 22 · see citing works
Baul, S., Ahmed, K. T., Jiang, Q., Wang, G., Li, Q., Yong, J., & Zhang, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae316 · Cited by 21 · see citing works
Detecting anomalous anatomic regions in spatial transcriptomics with STANDS
Xu, K., Lu, Y., Hou, S., Liu, K., Du, Y., Huang, M., Feng, H., Wu, H., & Sun, X. (2024) · Nature Communications · doi:10.1038/s41467-024-52445-9 · Cited by 20 · see citing works
Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data
Zuo, C., Zhu, J., Zou, J., & Chen, L. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70331 · Cited by 22 · see citing works
Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data
Tan, X., Mulay, O., Xie, J., MacDonald, S., Kim, T., Zhou, C., Xiong, Z., Tan, S. X., Ye, N., Reed, A. E. M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68487-0 · Cited by 18 · see citing works
SEPAL: Spatial Gene Expression Prediction from Local Graphs
Mejia, G., Cárdenas, P., Ruiz, D., Castillo, A., & Arbeláez, P. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00243 · Cited by 19 · see citing works
Chen, X., Xu, H., Yu, S., Hu, W., Zhang, Z., Wang, X., Yuan, Y., Wang, M., Chen, L., Lin, X., et al. (2025) · Biology · doi:10.3390/biology14060651 · Cited by 17 · see citing works
DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E ImagesPreprint
Nonchev, K., Dawo, S., Siliņa, K., Moch, H., Andani, S., Koelzer, V. H., & Rätsch, G. (2025) · medRxiv · doi:10.1101/2025.02.09.25321567 · Cited by 15 · see citing works
FmH2ST: foundation model-based spatial transcriptomics generation from histological images
Wang, Y., Wang, J., Xu, Y., Liu, N., Liu, B., Li, Y., & Yu, G. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf865 · Cited by 13 · see citing works
Wu, S., Qiu, Y., & Cheng, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad395 · Cited by 13 · see citing works
Yuan, L., Jiang, Y., Wang, Q., Hu, C., Li, G., & Zheng, C.-H. (2026) · BMC Genomics · doi:10.1186/s12864-026-12568-3 · Cited by 12 · see citing works
Ganguly, A., Chatterjee, D., Huang, W., Zhang, J., Yurovsky, A., Johnson, T. S., & Chen, C. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01455 · Cited by 11 · see citing works
Path2Omics Enhances Transcriptomic and Methylation Prediction Accuracy from Tumor Histopathology
Hoang, D.-T., Shulman, E. D., Dhruba, S. R., Nair, N. U., Barman, R. K., Cantore, T., Biswas, S., Lalchungnunga, H., Singh, O., Chung, Y., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-25-4350 · Cited by 11 · see citing works
Quantifying interpretation reproducibility in Vision Transformer models with TAVAC
Zhao, Y., Agyemang, D., Liu, Y., Mahoney, J. M., & Li, S. (2024) · Science Advances · doi:10.1126/sciadv.abg0264 · Cited by 11 · see citing works
Zeng, Y., Yin, R., Luo, M., Chen, J., Pan, Z., Lu, Y., Yu, W., & Yang, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.30.510297 · Cited by 11 · see citing works
Shulman, E. D., Campagnolo, E. M., Lodha, R., Stemmer, A., Cantore, T., Ru, B., Chang, T., Biswas, S., Dhruba, S. R., Wang, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.16.618609 · Cited by 11 · see citing works
High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE
Shi, Z., Xue, S., Zhu, F., & Min, W. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822048 · Cited by 10 · see citing works
HECLIP: histology-enhanced contrastive learning for imputation of transcriptomics profiles
Wang, Q., Chen, W.-J., Su, J., Wang, G., & Song, Q. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf363 · Cited by 10 · see citing works
Cross-Modal Diffusion Modelling for Super-Resolved Spatial Transcriptomics
Wang, X., Huang, X., Price, S. J., & Li, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72384-1_10 · Cited by 10 · see citing works
Yan, Q., Li, X., Cui, J., Rong, J., Zhang, J., Gao, P., Xu, Y., Qiu, F., & Zuo, C. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf317 · Cited by 9 · see citing works
DANet: spatial gene expression prediction from H&E histology images through dynamic alignment
Wu, Y., Xie, J., Nie, J., Cao, J., Zeng, Y., & Wang, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf422 · Cited by 8 · see citing works
Shi, Z., Zhu, F., & Min, W. (2025) · Methods · doi:10.1016/j.ymeth.2024.11.016 · Cited by 8 · see citing works
Computational Methods for Breast Cancer Molecular Profiling using Routine Histopathology: A Review
Kunhoth, S., Al-Maadeed, S. A., Akbari, Y., & Saady, R. M. A. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10374-w · Cited by 8 · see citing works
Hist2Cell: Deciphering fine-grained cellular architectures from histology images
Zhao, W., Liang, Z., Huang, X., Huang, Y., & Yu, L. (2026) · Cell Genomics · doi:10.1016/j.xgen.2025.101137 · Cited by 8 · see citing works
Thâalbi, O., & Akhloufi, M. A. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00489-8 · Cited by 8 · see citing works
Jiang, Y., Xie, J., Tan, X., Ye, N., & Nguyen, Q. H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.20.558624 · Cited by 8 · see citing works
Zai, K., Mukti, A. P., Notty, G. S., Niswah, S. U., Refliandi, & Oktivendra, F. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2025.126266 · Cited by 8 · see citing works
Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs
Song, T., Cosatto, E., Wang, G., Kuang, R., Gerstein, M., Min, M. R., & Warrell, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae383 · Cited by 7 · see citing works
Inferring single-cell spatial gene expression with tissue morphology via explainable deep learningPreprint
Zhao, Y., Alizadeh, E., Taha, H. B., Liu, Y., Xu, M., Mahoney, J. M., & Li, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.12.598686 · Cited by 7 · see citing works
Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion
Mejia, G., Ruiz, D., Cárdenas, P., Manrique, L., Vega, D., & Arbeláez, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72083-3_9 · Cited by 6 · see citing works
Li, B., Karami, M., Junayed, M. S., & Nabavi, S. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822051 · Cited by 6 · see citing works
Han, M.-H., Yang, D.-K., Cheng, J., Zhang, X.-K., Chen, Z.-Z., Kuang, H., & Zhang, L. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112458 · Cited by 3 · see citing works
sCellST predicts single-cell gene expression from H& E images
Chadoutaud, L., Lerousseau, M., Herrero-Saboya, D., Ostermaier, J., Fontugne, J., Barillot, E., & Walter, T. (2026) · Nature Communications · doi:10.1038/s41467-025-67965-1 · Cited by 6 · see citing works
Li, W., Zhang, D., Peng, E., Shen, S., Alinejad-Rokny, H., Liu, Y., Zheng, J., Jiang, C., & Ye, Y. (2026) · Advanced Science · doi:10.1002/advs.202514351 · Cited by 5 · see citing works
Linking transcriptome and morphology in bone cells at cellular resolution with generative AI
Lu, L., Ono, N., & Welch, J. D. (2024) · Journal of Bone and Mineral Research · doi:10.1093/jbmr/zjae151 · Cited by 5 · see citing works
Vispro improves imaging analysis for Visium spatial transcriptomics
Ma, H., Zhang, X., Qu, Y., Zhang, A. R., & Ji, Z. (2025) · Genome biology · doi:10.1186/s13059-025-03648-w · Cited by 3 · see citing works
Reg2ST: recognizing potential patterns from gene expression for spatial transcriptomics prediction
Wang, X., Wang, Y., Wang, X., Wang, Y., & Li, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf425 · Cited by 5 · see citing works
Spatial Gene Expression Prediction from Histology Images with STco
Shi, Z., Zhu, F., Wang, C., & Min, W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5128-0_8 · Cited by 5 · see citing works
Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeepPreprint
Jiang, X., Dong, L., Wang, S., Wen, Z., Chen, M., Xu, L., Xiao, G., & Li, Q. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.07.570715 · Cited by 5 · see citing works
Completing spatial transcriptomics data for gene expression prediction benchmarking
Ruiz, D., Cárdenas, P., Manrique, L., Vega, D., Mejia, G., & Arbeláez, P. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103754 · Cited by 4 · see citing works
Deep learning-based multimodal pathogenomics integration for precision cancer prognosis
Feng, X., Song, G., Zhang, Y., Guo, L., Jiang, Y., Gong, W., Feng, Y., Xu, C., Yang, Y., & He, M. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07682-5 · Cited by 4 · see citing works
HistoSPACE: Histology-inspired spatial transcriptome prediction and characterization engine
Kumar, S., & Chatterjee, S. (2024) · Methods · doi:10.1016/j.ymeth.2024.11.002 · Cited by 4 · see citing works
Benchmarking the translational potential of spatial gene expression prediction from histologyPreprint
Chan, A. S., Wang, C., Fu, X., Ghazanfar, S., Kim, J., Patrick, E., & Yang, J. Y. H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.12.571251 · Cited by 4 · see citing works
Multi-Modal Disentanglement of Spatial Transcriptomics and Histopathology ImagingPreprint
Maan, H., Ji, Z., Sicheri, E., Tan, T. J., Selega, A., Gonzalez, R., Krishnan, R. G., Wang, B., & Campbell, K. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.19.638201 · Cited by 4 · see citing works
Decrypting cancer's spatial code: from single cells to tissue niches
Celik, C., Shi, P., Withnell, E., Lam, H. W., & Secrier, M. (2025) · Molecular Oncology · doi:10.1002/1878-0261.70100 · Cited by 3 · see citing works
Deep Association Multimodal Learning for Zero-Shot Spatial Transcriptomics Prediction
Zhou, Y., Lu, Y., Li, Q., Li, X., & Wang, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04978-0_13 · Cited by 3 · see citing works
Spatially Gene Expression Prediction Using Dual-Scale Contrastive Learning
Qu, M., Wu, Y., Di, D., Gao, Y., Su, T., Song, Y., & Fan, L. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05182-0_56 · Cited by 3 · see citing works
Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics
Qu, M., Wu, Y., Di, D., Su, A., Su, T., Song, Y., & Fan, L. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822232 · Cited by 3 · see citing works
GeNetFormer: Transformer-Based Framework for Gene Expression Prediction in Breast Cancer
Thâalbi, O., & Akhloufi, M. A. (2025) · AI · doi:10.3390/ai6030043 · Cited by 3 · see citing works
Predicting Spatial Transcriptomics from H&E Image by Pretrained Contrastive Alignment LearningPreprint
Zou, J., Xiao, K., Chen, Z., Pei, J., Xu, J. L., Chen, T., Hou, L., Wu, C., She, Y., Yuan, Z., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.15.659438 · Cited by 3 · see citing works
Srinivasan, G., Le, M., Azher, Z., Liu, X., Vaickus, L. J., Kaur, H., Kolling, F., Palisoul, S. M., Perreard, L., Lau, K. S., et al. (2025) · Modern Pathology · doi:10.1016/j.modpat.2025.100866 · Cited by 2 · see citing works
Accurately Predicting Cell Type Abundance from Spatial Histology Image Through HPCell
Zhao, Y., Li, Y., Yu, W., Zhang, H., Wang, Z., Yang, Y., & Zeng, Y. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00757-9 · Cited by 2 · see citing works
Dang, T. M., Li, H., Guo, Y., Ma, H., Jiang, F., Miao, Y., Zhou, Q., Gao, J., & Huang, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04927-8_22 · Cited by 2 · see citing works
SciSt: single-cell reference-informed spatial gene expression prediction from pathological images
Li, Y., Zhong, F., & Liu, L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf613 · Cited by 2 · see citing works
Advances in predicting omics profiles from imaging data
Beachum, A. H., Xiao, X., Zhou, Y., Li, Q. Y., Xiao, G., & Xu, L. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag090 · Cited by 2 · see citing works
Data enhancement in the age of spatial biology
Liao, L., Martin, P. C. N., Kim, H., Panahandeh, S., & Won, K. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.06.008 · Cited by 2 · see citing works
Min, W., Xue, S., Zhu, F., Xu, T., Wang, C., & Xie, H.-B. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010087 · Cited by 2 · see citing works
HaDM-ST: Histology-Assisted Differential Modeling for Spatial Transcriptomics Generation
Liu, X., Jiang, Z., Zhu, P., Liu, H., & Li, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06624-4_14 · Cited by 2 · see citing works
Liu, A., Zhao, Y., Kim, W.-K., Shen, H., Ding, Z., & Deng, H. (2026) · Genome biology · doi:10.1186/s13059-026-04168-x · Cited by 2 · see citing works
Zhang, N., Long, Y., Xia, S., Jia, P., Jin, Y., Chen, S., Chen, J., & Li, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.07.669202 · Cited by 2 · see citing works
Self-citationZhan, G., Du, X., Liu, J., Li, Y., Lin, L., Li, J., & Chen, Y. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782295 · Cited by 1 · see citing works
A Fused Transformer-based Model for Gene Expression Prediction using Histopathology Images
Junayed, M. S., Jeny, A. A., Nabavi, S., & Măndoiu, I. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821923 · Cited by 1 · see citing works
Sun, W., Zhang, P., Xu, J., Zhang, W., Zeng, Y., & Li, L. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112207 · Cited by 1 · see citing works
Bridging imaging and genomics: Domain knowledge guided spatial transcriptomics analysis
Zhang, W., Liu, X., Chen, T., Xu, W., Sakal, C., Nie, X., Wang, L., & Li, X. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103746 · Cited by 1 · see citing works
Kaur, M., Kumar, A., Ceccarelli, M., Mall, R., & Gupta, S. (2025) · International Workshop on Multimedia Computing for Health and Medicine (MCHM) · doi:10.1145/3728424.3760768 · Cited by 1 · see citing works
DPM: A Deep Learning and Optimal Transport Framework for Cost-Effective Spatial Metabolomics
Yao, B., Yang, L., Zhang, C., Lv, Y., Yang, C., Di, L., Luo, J., & Lin, S. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.5c06903 · Cited by 1 · see citing works
Xu, R., Cheng, X., Ching, W., Wu, S., Zhang, Y., & Zhang, Y. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013893 · Cited by 1 · see citing works
Niu, Y., Liu, J., Zhan, Y., Shi, J., Zhang, D., Reinius, M., Machado, I. P., Crispin‐Ortuzar, M., Wu, J., Li, C., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104008 · Cited by 1 · see citing works
Yin, W., Peng, Q., Meng, F., Wan, Y., Zhang, W., & Zhou, Y. (2026) · Nature Computational Science · doi:10.1038/s43588-026-00992-0 · Cited by 1 · see citing works
Cheng, Y., Zhang, B., Edula, C. S. R., Sun, M., Zhang, Z., & Zhang, X. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3819011 · Cited by 1 · see citing works
Visual Computing Models in Cancer: PET/CT, CAD, CNN, and ST-Net
Yüksel, S., & Tombak, C. A. (2023) · Journal of Student Research · doi:10.47611/jsrhs.v12i2.4305 · Cited by 1 · see citing works
Yan, C., Ruan, Z., Chen, S., Pan, Y., Han, X., Li, Y., & Liu, J. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf201 · Cited by 1 · see citing works
DeepPathway: predicting pathway expression from histopathology images
Ahsan, M., Hanley, K. P., Fergie, M., O’Leary, C., Borst, G. R., Roncaroli, F., Minhas, F., Rattray, M., Iqbal, M., & Baker, S. M. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag643 · Cited by 1 · see citing works
Chadoutaud, L., Lerousseau, M., Herrero-Saboya, D., Ostermaier, J., Fontugne, J., Barillot, E., & Walter, T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.07.622225 · Cited by 1 · see citing works
- Authors
- Pang, M., Su, K., & Li, M.
- Affiliations
- King Abdullah University of Science and Technology (SA)
- University of Pennsylvania (US)
- Corresponding author
- Su, Kenong
- Li, Mingyao
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2021-11-28
- OpenAlex
- W3216067893
- Cited by
- 130
- References
- 37
- Citations by year
- 2026: 35 · 2025: 49 · 2024: 28 · 2023: 13 · 2022: 5
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- spatial transcriptomics, HER2-positive breast cancer, vision transformer, tumor molecular signatures
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