Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566 · see citing works

Highly accurate protein structure prediction with AlphaFold

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works

Semi-supervised classification with graph convolutional networks

Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works

Fast unfolding of communities in large networks

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works

Silhouettes: A graphical aid to the interpretation and validation of cluster analysis

Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392 · see citing works

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works

Least squares quantization in PCM

Lloyd, S. (1982) · IEEE Transactions on Information Theory · doi:10.1109/tit.1982.1056489 · Cited by 15,836 · see citing works

Untitled work

(2010) · Cited by 14,134 · see citing works

Untitled work

(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works

Approximation by superpositions of a sigmoidal function

Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883 · see citing works

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653 · see citing works

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works

Untitled work

(2010) · Cited by 8,261 · see citing works

Spatial reconstruction of single-cell gene expression data

Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · see citing works

Theory of edge detection

Marr, D., & Hildreth, E. (1980) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1980.0020 · Cited by 6,510 · see citing works

Approximation capabilities of multilayer feedforward networks

Hornik, K. (1991) · Neural Networks · doi:10.1016/0893-6080(91)90009-t · Cited by 6,091 · see citing works

From Louvain to Leiden: guaranteeing well-connected communities

Traag, V. A., Waltman, L., & van Eck, N. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-41695-z · Cited by 5,635 · see citing works

Genome-wide atlas of gene expression in the adult mouse brain

Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572 · see citing works

Object Detection With Deep Learning: A Review

Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545 · see citing works

An overview of gradient descent optimization algorithms

Ruder, S. (2016) · arXiv · doi:10.48550/arxiv.1609.04747 · Cited by 4,962 · see citing works

Convolutional networks for images, speech, and time series

LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,637 · see citing works

Visualization 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

Siamese Neural Networks for One-shot Image Recognition

Koch, G. E., Zemel, R. S., & Salakhutdinov, R. (2015) · Cited by 3,712 · see citing works

CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes

Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449 · see citing works

Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris

The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 3,326 · see citing works

Spatially resolved, highly multiplexed RNA profiling in single cells

Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969 · see citing works

Deep generative modeling for single-cell transcriptomics

Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530 · 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

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors

Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works

TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

Zhu, X., Lyu, S., Wang, X., & Zhao, Q. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00312 · Cited by 2,382 · see citing works

The human cell atlas

Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works

VoxelMorph: A Learning Framework for Deformable Medical Image Registration

Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J. V., & Dalca, A. V. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2897538 · Cited by 2,199 · see citing works

Imaging individual mRNA molecules using multiple singly labeled probes

Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A., & Tyagi, S. (2008) · Nature Methods · doi:10.1038/nmeth.1253 · Cited by 2,028 · see citing works

RNA sequencing: the teenage years

Stark, R., Grzelak, M., & Hadfield, J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0150-2 · Cited by 1,906 · see citing works

A universal SNP and small-indel variant caller using deep neural networks

Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4235 · Cited by 1,840 · see citing works

Single-cell RNA sequencing technologies and bioinformatics pipelines

Hwang, B., Lee, J. H., & Bang, D. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0071-8 · Cited by 1,793 · see citing works

A single-cell and spatially resolved atlas of human breast cancers

Wu, S. Z., Al-Eryani, G., Roden, D. L., Junankar, S., Harvey, K., Andersson, A., Thennavan, A., Wang, C., Torpy, J. R., Bartonicek, N., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00911-1 · Cited by 1,775 · see citing works

Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH

Eng, C.-H. L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.-C., et al. (2019) · Nature · doi:10.1038/s41586-019-1049-y · Cited by 1,754 · see citing works

Adult mouse cortical cell taxonomy revealed by single cell transcriptomics

Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works

Exploring tissue architecture using spatial transcriptomics

Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700 · see citing works

Early Stopping - But When?

Prechelt, L. (1998) · Lecture Notes in Computer Science · doi:10.1007/3-540-49430-8_3 · Cited by 1,649 · see citing works

Ten years of next-generation sequencing technology

van Dijk, E., Auger, H., Jaszczyszyn, Y., & Thermes, C. (2014) · Trends in Genetics · doi:10.1016/j.tig.2014.07.001 · Cited by 1,632 · see citing works

Three-dimensional intact-tissue sequencing of single-cell transcriptional states

Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620 · see citing works

Mapping the Mouse Cell Atlas by Microwell-Seq

Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works

Visualization of Single RNA Transcripts in Situ

Femino, A. M., Fay, F. S., Fogarty, K., & Singer, R. H. (1998) · Science · doi:10.1126/science.280.5363.585 · Cited by 1,504 · see citing works

The Technology and Biology of Single-Cell RNA Sequencing

Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487 · see citing works

Robust decomposition of cell type mixtures in spatial transcriptomics

Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-00830-w · Cited by 1,487 · see citing works

The Human Cell Atlas

Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457 · see citing works

Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq

Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342 · see citing works

Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution

Masuda, T., Sankowski, R., Staszewski, O., Böttcher, C., Amann, L., Sagar, Scheiwe, C., Nessler, S., Kunz, P., van Loo, G., et al. (2019) · Nature · doi:10.1038/s41586-019-0924-x · Cited by 1,310 · see citing works

Cell2location maps fine-grained cell types in spatial transcriptomics

Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Elmentaite, R., Lomakin, A., Kedlian, V., Gayoso, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01139-4 · Cited by 1,279 · see citing works

Deciphering cell–cell interactions and communication from gene expression

Armingol, E., Officer, A., Harismendy, O., & Lewis, N. E. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00292-x · Cited by 1,267 · see citing works

Museum of spatial transcriptomics

Moses, L., & Pachter, L. (2022) · Nature Methods · doi:10.1038/s41592-022-01409-2 · Cited by 1,255 · see citing works

Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

Chen, T., & Chen, H. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.392253 · Cited by 1,230 · see citing works

Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222 · see citing works

Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity

Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193 · see citing works

Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity

van Galen, P., Hovestadt, V., Wadsworth II, M. H., Hughes, T. K., Griffin, G. K., Battaglia, S., Verga, J. A., Stephansky, J., Pastika, T. J., Lombardi Story, J., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.031 · Cited by 1,164 · 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 · see citing works

SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

Hu, 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

FUNDAMENTALS OF PLANARIAN REGENERATION

Reddien, P. W., & Alvarado, A. S. (2004) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cellbio.20.010403.095114 · Cited by 1,108 · see citing works

Single-cell RNA-seq denoising using a deep count autoencoder

Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019) · Nature Communications · doi:10.1038/s41467-018-07931-2 · Cited by 1,084 · see citing works

A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications

Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works

Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas

Moncada, R., Barkley, D., Wagner, F., Chiodin, M., Devlin, J. C., Baron, M., Hajdu, C. H., Simeone, D. M., & Yanai, I. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0392-8 · Cited by 1,045 · see citing works

Spatial transcriptomics at subspot resolution with BayesSpace

Zhao, E. J., Stone, M. R., Ren, X., Guenthoer, J., Smythe, K. S., Pulliam, T., Williams, S. R., Uytingco, C. R., Taylor, S. E. B., Nghiem, P., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00935-2 · Cited by 988 · see citing works

High-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 · see citing works

In situ sequencing for RNA analysis in preserved tissue and cells

Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wählby, C., & Nilsson, M. (2013) · Nature Methods · doi:10.1038/nmeth.2563 · Cited by 934 · see citing works

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

Biancalani, T., Scalia, G., Buffoni, L., Avasthi, R., Lu, Z., Sanger, A., Tokcan, N., Vanderburg, C. R., Segerstolpe, Å., Zhang, M., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01264-7 · Cited by 931 · see citing works

Giotto: a toolbox for integrative analysis and visualization of spatial expression data

Dries, R., Zhu, Q., Dong, R., Eng, C.-H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02286-2 · Cited by 921 · see citing works

Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex

Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2967 · Cited by 920 · see citing works

Systematic comparison of single-cell and single-nucleus RNA-sequencing methods

Ding, J., Adiconis, X., Simmons, S. K., Kowalczyk, M. S., Hession, C. C., Marjanovic, N. D., Hughes, T. K., Wadsworth, M. H., Burks, T., Nguyen, L. T., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0465-8 · Cited by 871 · see citing works

Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies

Hochreiter, S., Bengio, Y., Informatik, F. F., Bengio, Y., Frasconi, P., & Schmidhuber, J. (2001) · Cited by 863 · see citing works

A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain

Davie, K., Janssens, J., Koldere, D., De Waegeneer, M., Pech, U., Kreft, Ł., Aibar, S., Makhzami, S., Christiaens, V., Bravo González-Blas, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.057 · Cited by 840 · see citing works

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets

Delorey, T. M., Ziegler, C. G. K., Heimberg, G., Normand, R., Yang, Y., Segerstolpe, Å., Abbondanza, D., Fleming, S. J., Subramanian, A., Montoro, D. T., et al. (2021) · Nature · doi:10.1038/s41586-021-03570-8 · Cited by 809 · see citing works

Canonical analysis of several sets of variables

Kettenring, J. R. (1971) · Biometrika · doi:10.1093/biomet/58.3.433 · Cited by 798 · see citing works

Single-cell multi-omics analysis of the immune response in COVID-19

Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID-19 BioResource Collaboration, Stephenson, E., Reynolds, G., Botting, R. A., Calero-Nieto, F. J., Morgan, M. D., Tuong, Z. K., Bach, K., Sungnak, W., Worlock, K. B., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01329-2 · Cited by 742 · see citing works

A 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

SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes

Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I., & Heyn, H. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab043 · Cited by 732 · see citing works

VoxelMorph: A Learning Framework for Deformable Medical Image RegistrationPreprint

Balakrishnan, G., Zhao, A. T., Sabuncu, M. R., Guttag, J. V., & Dalca, A. V. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 728 · see citing works

Validation of noise models for single-cell transcriptomics

Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721 · see citing works

Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

Pham, D., Tan, X., Balderson, B., Xu, J., Grice, L. F., Yoon, S., Willis, E. F., Tran, M., Lam, P. Y., Raghubar, A. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43120-6 · Cited by 716 · see citing works

SpatialDE: identification of spatially variable genes

Svensson, V., Teichmann, S. A., & Stegle, O. (2018) · Nature Methods · doi:10.1038/nmeth.4636 · Cited by 695 · see citing works

Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements

Tustison, N. J., Cook, P. A., Klein, A., Song, G., Das, S. R., Duda, J. T., Kandel, B. M., van Strien, N., Stone, J. R., Gee, J. C., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.044 · Cited by 670 · 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

Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro

(2021) · doi:10.1038/s41588-021-00972-2 · Cited by 662 · see citing works

Spatial organization of the somatosensory cortex revealed by osmFISH

Codeluppi, S., Borm, L. E., Zeisel, A., Manno, G. L., van Lunteren, J. A., Svensson, C. I., & Linnarsson, S. (2018) · Nature Methods · doi:10.1038/s41592-018-0175-z · Cited by 649 · see citing works

Wasserstein Auto-Encoders

Tolstikhin, I., Bousquet, O., Gelly, S., & Schölkopf, B. (2018) · MPG.PuRe (Max Planck Society) · Cited by 602 · see citing works

Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19

Su, Y., Chen, D., Yuan, D., Lausted, C. G., Choi, J., Dai, C. L., Voillet, V., Duvvuri, V. R. S. K., Scherler, K., Troisch, P., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.037 · Cited by 591 · see citing works

Universal Approximation Using Feedforward Neural Networks: A Survey of Some Existing Methods, and Some New Results

Scarselli, F., & Tsoi, A. C. (1998) · Neural Networks · doi:10.1016/s0893-6080(97)00097-x · Cited by 571 · see citing works

Single-Cell Transcriptional Profiling Reveals Cellular Diversity and Intercommunication in the Mouse Heart

Skelly, D. A., Squiers, G. T., McLellan, M. A., Bolisetty, M. T., Robson, P., Rosenthal, N. A., & Pinto, A. R. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.072 · Cited by 551 · see citing works

Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity

Berglund, E., Maaskola, J., Schultz, N., Friedrich, S., Marklund, M., Bergenstråhle, J., Tarish, F., Tanoglidi, A., Vicković, S., Larsson, L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04724-5 · Cited by 550 · see citing works

A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution

Packer, J. S., Zhu, Q., Huynh, C., Sivaramakrishnan, P., Preston, E. A., Dueck, H. R., Stefanik, D. J., Tan, K., Trapnell, C., Kim, J., et al. (2019) · Science · doi:10.1126/science.aax1971 · Cited by 539 · see citing works

Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues

Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Ferrante, T. C., Terry, R., Turczyk, B. M., Yang, J. L., Lee, H. S., Aach, J., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2014.191 · Cited by 529 · see citing works

Spatiotemporal analysis of human intestinal development at single-cell resolution

Fawkner-Corbett, D., Antanaviciute, A., Parikh, K., Jagielowicz, M., Gerós, A. S., Gupta, T., Ashley, N., Khamis, D., Fowler, D. J., Morrissey, E. R., et al. (2021) · Cell · doi:10.1016/j.cell.2020.12.016 · Cited by 528 · see citing works

Single-cell multiomics: technologies and data analysis methods

Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524 · see citing works

CONNECTIONIST LEARNING PROCEDURES11This chapter appeared in Volume 40 of Artificial Intelligence in 1989, reprinted with permission of North-Holland Publishing. It is a revised version of Technical Report CMU-CS-87-115, which has the same title and was prepared in June 1987 while the author was at Carnegie Mellon University. The research was supported by contract N00014-86-K-00167 from the Office of Naval Research and by grant IST-8520359 from the National Science Foundation.

Hinton, G. E. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051055-2.50029-8 · Cited by 523 · see citing works

Spatially informed cell-type deconvolution for spatial transcriptomics

Ma, Y., & Zhou, X. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01273-7 · Cited by 515 · 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

Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH

Zhang, M., Eichhorn, S. W., Zingg, B., Yao, Z., Cotter, K., Zeng, H., Dong, H., & Zhuang, X. (2021) · Nature · doi:10.1038/s41586-021-03705-x · Cited by 498 · see citing works

Single cell transcriptomic landscape of diabetic foot ulcers

Theocharidis, G., Thomas, B. E., Sarkar, D., Mumme, H., Pilcher, W., Dwivedi, B., Sandoval-Schaefer, T., Sîrbulescu, R. F., Kafanas, A., Mezghani, I., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27801-8 · Cited by 483 · see citing works

Single-cell transcriptomic characterization of a gastrulating human embryo

Tyser, R. C. V., Mahammadov, E., Nakanoh, S., Vallier, L., Scialdone, A., & Srinivas, S. (2021) · Nature · doi:10.1038/s41586-021-04158-y · Cited by 467 · see citing works

Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography

Andersson, A., Bergenstråhle, J., Asp, M., Bergenstråhle, L., Jurek, A., Navarro, J. F., & Lundeberg, J. (2020) · Communications Biology · doi:10.1038/s42003-020-01247-y · Cited by 463 · see citing works

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456 · see citing works

Technologies for Single-Cell Isolation

Gross, A., Schoendube, J., Zimmermann, S., Steeb, M., Zengerle, R., & Koltay, P. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms160816897 · Cited by 450 · see citing works

Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity

Joost, S., Zeisel, A., Jacob, T., Sun, X., La Manno, G., Lönnerberg, P., Linnarsson, S., & Kasper, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.010 · Cited by 429 · 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

In situ detection and genotyping of individual mRNA molecules

Larsson, C., Grundberg, I., Söderberg, O., & Nilsson, M. F. (2010) · Nature Methods · doi:10.1038/nmeth.1448 · Cited by 415 · see citing works

Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems

Alon, S., Goodwin, D., Sinha, A., Wassie, A. T., Chen, F., Daugharthy, E. R., Bando, Y., Kajita, A., Xue, A. G., Marrett, K., et al. (2021) · Science · doi:10.1126/science.aax2656 · Cited by 413 · see citing works

Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis

Li, X., Wang, K., Lyu, Y., Pan, H., Zhang, J., Stambolian, D. E., Suszták, K., Reilly, M. P., Hu, G., & Li, M. (2020) · Nature Communications · doi:10.1038/s41467-020-15851-3 · Cited by 408 · see citing works

Lung Single-Cell Signaling Interaction Map Reveals Basophil Role in Macrophage Imprinting

Cohen, M., Giladi, A., Gorki, A.-D., Gelbard-Solodkin, D., Zada, M., Hladik, A., Miklosi, A., Salame, T., Halpern, K. B., David, E., et al. (2018) · Cell · doi:10.1016/j.cell.2018.09.009 · Cited by 407 · see citing works

Improved Variational Autoencoders for Text Modeling using Dilated ConvolutionsPreprint

Yang, Z., Hu, Z., Salakhutdinov, R., & Berg-Kirkpatrick, T. (2017) · International Conference on Machine Learning · doi:10.48550/arxiv.1702.08139 · Cited by 405 · see citing works

Single‐cell RNA sequencing in cancer research

Zhang, Y., Wang, D., Miao, P., Tang, L. M., Ouyang, J., Xiong, F., Guo, C. J., Tang, Y., Zhou, Y., Liao, Q., et al. (2021) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-021-01874-1 · Cited by 398 · see citing works

High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin

Achim, K., Pettit, J.-B., Saraiva, L. R., Gavriouchkina, D., Larsson, T., Arendt, D., & Marioni, J. C. (2015) · Nature Biotechnology · doi:10.1038/nbt.3209 · Cited by 395 · see citing works

Inferring spatial and signaling relationships between cells from single cell transcriptomic data

Cang, Z., & Nie, Q. H. (2020) · Nature Communications · doi:10.1038/s41467-020-15968-5 · Cited by 393 · see citing works

SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics

Cabello‐Aguilar, S., Alame, M., Kon-Sun-Tack, F., Fau, C., Lacroix, M., & Colinge, J. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa183 · Cited by 390 · see citing works

Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems

Zhang, X., Li, T., Liu, F., Chen, Y., Yao, J., Li, Z., Huang, Y., & Wang, J. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.10.020 · Cited by 380 · see citing works

The Drosophila embryo at single-cell transcriptome resolution

Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., Kocks, C., Rajewsky, N., & Zinzen, R. P. (2017) · Science · doi:10.1126/science.aan3235 · Cited by 371 · see citing works

Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19

Bernardes, J. P., Mishra, N., Tran, F., Bahmer, T., Best, L., Blase, J. I., Bordoni, D., Franzenburg, J., Geisen, U. M., Josephs‐Spaulding, J., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.11.017 · Cited by 353 · see citing works

Identification of spatial expression trends in single-cell gene expression data

Edsgärd, D., Johnsson, P., & Sandberg, R. (2018) · Nature Methods · doi:10.1038/nmeth.4634 · Cited by 342 · see citing works

Molecular atlas of the adult mouse brain

Ortiz, C., Navarro, J. F., Jurek, A., Märtin, A., Lundeberg, J., & Meletis, K. (2020) · Science Advances · doi:10.1126/sciadv.abb3446 · Cited by 317 · see citing works

Spatially resolved transcriptomics adds a new dimension to genomics

Larsson, L., Frisén, J., & Lundeberg, J. (2021) · Nature Methods · doi:10.1038/s41592-020-01038-7 · Cited by 313 · see citing works

Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue

Gyllborg, D., Langseth, C. M., Qian, X., Choi, E., Salas, S. M., Hilscher, M. M., Lein, E. S., & Nilsson, M. F. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa792 · Cited by 306 · see citing works

Explainable multiview framework for dissecting spatial relationships from highly multiplexed data

(2022) · doi:10.1186/s13059-022-02663-5 · Cited by 297 · see citing works

SpatialDWLS: accurate deconvolution of spatial transcriptomic data

Dong, R., & Yuan, G. (2021) · Genome biology · doi:10.1186/s13059-021-02362-7 · Cited by 292 · see citing works

Effects of Hidden Layers on the Efficiency of Neural networks

Uzair, M., & Jamil, N. (2020) · IEEE International Multitopic Conference (INMIC) · doi:10.1109/inmic50486.2020.9318195 · Cited by 289 · see citing works

Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface

Hunter, M. V., Moncada, R. M., Weiss, J. M., Yanai, I., & White, R. M. (2021) · Nature Communications · doi:10.1038/s41467-021-26614-z · Cited by 288 · see citing works

DestVI identifies continuums of cell types in spatial transcriptomics data

Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01272-8 · Cited by 276 · see citing works

A Hybrid Convolutional Variational Autoencoder for Text Generation

Semeniuta, S., Severyn, A., & Barth, E. (2017) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1066 · Cited by 272 · see citing works

Gene expression cartography

Nitzan, M., Karaiskos, N., Friedman, N., & Rajewsky, N. (2019) · Nature · doi:10.1038/s41586-019-1773-3 · Cited by 272 · see citing works

scVAE: variational auto-encoders for single-cell gene expression data

Grønbech, C. H., Vording, M. F., Timshel, P. N., Sønderby, C. K., Pers, T. H., & Winther, O. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa293 · Cited by 268 · see citing works

Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

Marouf, M., Machart, P., Bansal, V., Kilian, C., Magruder, D. S., Krebs, C. F., & Bonn, S. (2020) · Nature Communications · doi:10.1038/s41467-019-14018-z · Cited by 256 · see citing works

ACTINN: automated identification of cell types in single cell RNA sequencing

Ma, F., & Pellegrini, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz592 · Cited by 252 · see citing works

Automated methods for cell type annotation on scRNA-seq data

Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.015 · Cited by 249 · see citing works

Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data

Zhu, Q., Shah, S., Dries, R., Cai, L., & Yuan, G. (2018) · Nature Biotechnology · doi:10.1038/nbt.4260 · Cited by 248 · see citing works

Advances in spatial transcriptomic data analysisPreprint

Dries, R., Chen, J. G., Rossi, N. D., Khan, M. M., Sistig, A., & Yuan, G. (2021) · Genome Research · doi:10.1101/gr.275224.121 · Cited by 236 · see citing works

Severely ill patients with COVID-19 display impaired exhaustion features in SARS-CoV-2–reactive CD8 + T cells

Kusnadi, A., Ramírez-Suástegui, C., Fajardo, V., Chee, S., Meckiff, B. J., Simon, H. A., Pelosi, E., Seumois, G., Ay, F., Vijayanand, P., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abe4782 · Cited by 232 · see citing works

DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence

Song, Q., & Su, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa414 · Cited by 224 · see citing works

Efficient in situ barcode sequencing using padlock probe-based BaristaSeq

Chen, X., Sun, Y.-C., Church, G. M., Lee, J. H., & Zador, A. M. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1206 · Cited by 213 · see citing works

Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin

Bäckdahl, J., Franzén, L., Massier, L., Li, Q., Jalkanen, J., Gao, H., Andersson, A., Bhalla, N., Thorell, A., Rydén, M., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.07.018 · Cited by 212 · see citing works

Defining human cardiac transcription factor hierarchies using integrated single-cell heterogeneity analysis

Churko, J. M., Garg, P., Treutlein, B., Venkatasubramanian, M., Wu, H., Lee, J., Wessells, Q. N., Chen, S.-Y., Chen, W.-Y., Chetal, K., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07333-4 · Cited by 207 · see citing works

Lagging Inference Networks and Posterior Collapse in Variational AutoencodersPreprint

He, J., Spokoyny, D., Neubig, G., & Berg-Kirkpatrick, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.05534 · Cited by 203 · see citing works

Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections

Salmén, F., Ståhl, P. L., Mollbrink, A., Navarro, J. F., Vicković, S., Frisén, J., & Lundeberg, J. (2018) · Nature Protocols · doi:10.1038/s41596-018-0045-2 · Cited by 193 · see citing works

Single cell molecular alterations reveal target cells and pathways of concussive brain injury

Arneson, D., Zhang, G., Ying, Z., Zhuang, Y., Byun, H. R., Ahn, I. S., Gomez-Pinilla, F., & Yang, X. (2018) · Nature Communications · doi:10.1038/s41467-018-06222-0 · Cited by 193 · see citing works

Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions

Butler, D., Mozsary, C., Meydan, C., Foox, J., Rosiene, J. A., Shaiber, A., Danko, D., Afshinnekoo, E., MacKay, M. J., Sedlazeck, F. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21361-7 · Cited by 191 · see citing works

Neurological Manifestations of COVID-19 Feature T Cell Exhaustion and Dedifferentiated Monocytes in Cerebrospinal Fluid

Heming, M., Li, X., Räuber, S. J., Mausberg, A. K., Börsch, A., Hartlehnert, M., Singhal, A., Lu, I., Fleischer, M., Szepanowski, F., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.12.011 · Cited by 179 · see citing works

Deep learning applications in single-cell genomics and transcriptomics data analysis

Erfanian, N., Heydari, A. A., Feriz, A. M., Picazo, P. I., Derakhshani, A., GhasemiGol, M., Farahpour, M., Razavi, S. M., Nasseri, S., Safarpour, H., et al. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.115077 · Cited by 162 · see citing works

Hsa-let-7c controls the committed differentiation of IGF-1-treated mesenchymal stem cells derived from dental pulps by targeting IGF-1R via the MAPK pathways

Liu, G.-X., Ma, S., Li, Y., Yu, Y., Zhou, Y.-X., Lu, Y.-D., Lin, J., Wang, Z.-L., & Yu, J.-H. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0048-7 · Cited by 158 · see citing works

Technical Review: In Situ Hybridization

Jensen, E. C. (2014) · The Anatomical Record · doi:10.1002/ar.22944 · Cited by 158 · see citing works

scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network

Shao, X., Yang, H., Zhuang, X., Liao, J., Yang, P., Cheng, J., Lu, X., Chen, H., & Fan, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab775 · Cited by 158 · see citing works

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

Pardo, B., Spangler, A., Weber, L. M., Page, S. C., Hicks, S. C., Jaffe, A. E., Martinowich, K., Maynard, K. R., & Collado‐Torres, L. (2022) · BMC Genomics · doi:10.1186/s12864-022-08601-w · Cited by 157 · see citing works

Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis

Hu, J., Li, X., Hu, G., Lyu, Y., Suszták, K., & Li, M. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00233-7 · Cited by 145 · see citing works

Upper cortical layer–driven network impairment in schizophrenia

Batiuk, M. Y., Tyler, T., Dragičević, K., Mei, S., Rydbirk, R., Petukhov, V., Deviatiiarov, R. M., Sedmak, D., Frank, E., Feher, V., et al. (2022) · Science Advances · doi:10.1126/sciadv.abn8367 · Cited by 141 · see citing works

Deep Residual Networks with Exponential Linear Unit

Shah, A. U., Kadam, E., Shah, H., Shinde, S., & Shingade, S. (2016) · International Symposium on Computer Vision and the Internet (VisionNet) · doi:10.1145/2983402.2983406 · Cited by 117 · see citing works

DNA Microscopy: Optics-free Spatio-genetic Imaging by a Stand-Alone Chemical Reaction

Weinstein, J. A., Regev, A., & Zhang, F. (2019) · Cell · doi:10.1016/j.cell.2019.05.019 · Cited by 111 · see citing works

Gating mass cytometry data by deep learning

Li, H., Shaham, U., Stanton, K. P., Yao, Y., Montgomery, R. R., & Kluger, Y. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx448 · Cited by 110 · see citing works

Joint cell segmentation and cell type annotation for spatial transcriptomics

Littman, R., Hemminger, Z., Foreman, R., Arneson, D. V., Zhang, G., Gómez‐Pinilla, F., Yang, X., & Wollman, R. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010108 · Cited by 96 · see citing works

Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium

Vicković, S., Schapiro, D., Carlberg, K., Lötstedt, B., Larsson, L., Hildebrandt, F., Korotkova, M., Hensvold, A. H., Catrina, A. I., Sorger, P. K., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03050-3 · Cited by 84 · 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

SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells

Tan, X., Su, A., Tran, M., & Nguyen, Q. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz914 · Cited by 76 · see citing works

Exploring inflammatory signatures in arthritic joint biopsies with Spatial Transcriptomics

Carlberg, K., Korotkova, M., Larsson, L., Catrina, A. I., Ståhl, P. L., & Malmström, V. (2019) · Scientific Reports · doi:10.1038/s41598-019-55441-y · Cited by 73 · see citing works

Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

Gupta, R. K., & Kuźnicki, J. (2020) · Cells · doi:10.3390/cells9081751 · Cited by 70 · see citing works

An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries

Jemt, A., Salmén, F., Lundmark, A., Mollbrink, A., Navarro, J. F., Ståhl, P. L., Yucel‐Lindberg, T., & Lundeberg, J. (2016) · Scientific Reports · doi:10.1038/srep37137 · Cited by 67 · see citing works

SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss FunctionsPreprint

Heydari, A. A., Thompson, C. A., & Mehmood, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12355 · Cited by 66 · see citing works

Imputation of single‐cell gene expression with an autoencoder neural network

Badsha, M. B., Li, R., Liu, B., Li, Y., Xian, M., Banovich, N. E., & Fu, A. Q. (2020) · Quantitative Biology · doi:10.1007/s40484-019-0192-7 · Cited by 53 · see citing works

Single-Cell Transcriptome Analysis of Colon Cancer Cell Response to 5-Fluorouracil-Induced DNA Damage

Park, S. R., Namkoong, S., Friesen, L., Cho, C., Zhang, Z. Z., Chen, Y., Yoon, E., Kim, C. H., Kwak, H., Kang, H. M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108077 · Cited by 53 · see citing works

Molecular Subtyping of Serous Ovarian Cancer Based on Multi-omics Data

Zhang, Z., Huang, K., Gu, C., Zhao, L., Wang, N., Wang, X., Zhao, D., Zhang, C., Lu, Y., & Meng, Y. (2016) · Scientific Reports · doi:10.1038/srep26001 · Cited by 51 · see citing works

Deep Convolutional and Recurrent Neural Networks for Cell Motility Discrimination and Prediction

Kimmel, J. C., Brack, A. S., & Marshall, W. F. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2919307 · Cited by 49 · see citing works

SpotLearn: Convolutional Neural Network for Detection of Fluorescence In Situ Hybridization (FISH) Signals in High-Throughput Imaging ApproachesPreprint

Gudla, P. R., Nakayama, K., Pegoraro, G., & Misteli, T. (2017) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2017.82.033761 · Cited by 46 · see citing works

Harnessing Single-Cell RNA Sequencing to Better Understand How Diseased Cells Behave the Way They Do in Cardiovascular Disease

Iqbal, F., Lupieri, A., Aikawa, M., & Aikawa, E. R. (2020) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.120.314776 · Cited by 44 · see citing works

Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder

Daniel, T., & Tamar, A. (2021) · 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/cvpr46437.2021.00437 · Cited by 44 · see citing works

Microfluidics for genome-wide studies involving next generation sequencing

Ma, S., Murphy, T. W., & Lu, C. (2017) · Biomicrofluidics · doi:10.1063/1.4978426 · Cited by 41 · see citing works

Quantitative approaches for investigating the spatial context of gene expression

Lee, J. H. (2016) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1369 · Cited by 36 · see citing works

ACTIVA : realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders

Heydari, A. A., Davalos, O. A., Zhao, L., Hoyer, K. K., & Sindi, S. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac095 · Cited by 35 · see citing works

Automated identification of the mouse brain’s spatial compartments from in situ sequencing data

Partel, G., Hilscher, M. M., Milli, G., Solorzano, L., Klemm, A. H., Nilsson, M. F., & Wählby, C. (2020) · BMC Biology · doi:10.1186/s12915-020-00874-5 · Cited by 33 · see citing works

DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data

Maseda, F., Cang, Z., & Nie, Q. H. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.636743 · Cited by 33 · see citing works

Semantic segmentation of mFISH images using convolutional networks

Pardo, E., Morgado, J. M., & Malpica, N. (2018) · Cytometry Part A · doi:10.1002/cyto.a.23375 · Cited by 28 · see citing works

Regulation of CTLA-4 and PD-L1 Expression in Relapsing-Remitting Multiple Sclerosis Patients after Treatment with Fingolimod, IFNβ-1α, Glatiramer Acetate, and Dimethyl Fumarate Drugs

Derakhshani, A., Asadzadeh, Z., Safarpour, H., Leone, P., Shadbad, M. A., Heydari, A. A., Baradaran, B., & Racanelli, V. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11080721 · Cited by 28 · see citing works

Technical Advances in Single-Cell RNA Sequencing and Applications in Normal and Malignant Hematopoiesis

Huang, X., Li, X., Qin, P., Zhu, Y., Xu, S., & Chen, J. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00582 · Cited by 26 · see citing works

Principles of Spatial Transcriptomics Analysis: A Practical Walk-Through in Kidney Tissue

Noel, T., Wang, Q. S., Greka, A., & Marshall, J. L. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2021.809346 · Cited by 26 · see citing works

Spatially resolved transcriptomics reveals the architecture of the tumor/microenvironment interfacePreprint

Hunter, M. V., Moncada, R. M., Weiss, J. M., Yanai, I., & White, R. M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.05.368753 · Cited by 24 · see citing works

Existence and uniqueness of the maximum likelihood estimator for the two-parameter negative binomial distribution

Aragón, J., Eberly, D. H., & Eberly, S. I. (1992) · Statistics & Probability Letters · doi:10.1016/0167-7152(92)90157-z · Cited by 23 · see citing works

In Situ Sequencing: A High-Throughput, Multi-Targeted Gene Expression Profiling Technique for Cell Typing in Tissue Sections

Hilscher, M. M., Gyllborg, D., Yokota, C., & Nilsson, M. F. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0623-0_20 · Cited by 22 · see citing works

On the Generalization Benefit of Noise in Stochastic Gradient DescentPreprint

Smith, S. L., Elsen, E., & De, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15081 · Cited by 22 · see citing works

GLUER: integrative analysis of single-cell omics and imaging data by deep neural networkPreprint

Peng, T., Chen, G. M., & Tan, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.25.427845 · Cited by 21 · see citing works

Spatial organization of the somatosensory cortex revealed by cyclic smFISHPreprint

Codeluppi, S., Borm, L. E., Zeisel, A., Manno, G. L., van Lunteren, J. A., Svensson, C. I., & Linnarsson, S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/276097 · Cited by 19 · see citing works

Deep learning applications in single-cell genomics and transcriptomics data analysis

Erfanian, N., Heydari, A. A., Feriz, A. M., Picazo, P. I., Derakhshani, A., GhasemiGol, M., Farahpour, M., Razavi, S. M., Nasseri, S., Safarpour, H., et al. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.115077 · Cited by 162 · see citing works

Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective

Ge, S., Sun, S., Xu, H., Cheng, Q. S., & Ren, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf136 · Cited by 89 · see citing works

A guidebook of spatial transcriptomic technologies, data resources and analysis approaches

Yue, L., Liu, F., Hu, J., Yang, P., Wang, Y., Dong, J., Shu, W., Huang, X., & Wang, S. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.01.016 · Cited by 80 · see citing works

Microsystem Advances through Integration with Artificial Intelligence

Tsai, H., Podder, S., & Chen, P. (2023) · Micromachines · doi:10.3390/mi14040826 · Cited by 69 · see citing works

Deep Learning in Diverse Intelligent Sensor Based Systems

Zhu, Y., Wang, M., Yin, X., Zhang, J., Meijering, E. H. W., & Hu, J. (2022) · Sensors · doi:10.3390/s23010062 · Cited by 67 · 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

TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics data

Pielawski, N., Andersson, A., Avenel, C., Beháňová, A., Chelebian, E., Klemm, A. H., Nysjö, F., Solorzano, L., & Wählby, C. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15306 · Cited by 52 · see citing works

A review on deep learning applications in highly multiplexed tissue imaging data analysis

Zidane, M., Makky, A., Bruhns, M., Rochwarger, A., Babaei, S., Claassen, M., & Schürch, C. M. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1159381 · Cited by 30 · see citing works

From tissue architecture to clinical insights: Spatial transcriptomics in solid tumor studies

Sharma, A., Raut, S. S., Shukla, A., Gupta, S., Mishra, A., & Singh, A. (2025) · Seminars in Oncology · doi:10.1016/j.seminoncol.2025.152389 · Cited by 29 · see citing works

Role of next‐generation sequencing in revolutionizing healthcare for cancer management

Sahoo, O. S., Aidasani, H., Nayek, A., Tripathi, S., Talukdar, J., Gul, A., Kumar, D., Dhar, R., & Karmakar, S. (2024) · MedComm – Future Medicine · doi:10.1002/mef2.70001 · Cited by 25 · see citing works

Spatial transcriptomic analysis of the mouse brain following chronic social defeat stress

Wang, T., Song, Z., Zhao, X., Wu, Y., Wu, L., Haghparast, A., & Wu, H. (2023) · Exploration · doi:10.1002/exp.20220133 · Cited by 20 · see citing works

The Development of Plant Genome Sequencing Technology and Its Conservation and Application in Endangered Gymnosperms

Hong, K., Radian, Y., Manda, T., Xu, H., & Luo, Y. (2023) · Plants · doi:10.3390/plants12234006 · Cited by 16 · see citing works

TissUUmaps 3: Improvements in interactive visualization, exploration, and quality assessment of large-scale spatial omics dataPreprint

Pielawski, N., Andersson, A., Avenel, C., Beháňová, A., Chelebian, E., Klemm, A. H., Nysjö, F., Solorzano, L., & Wählby, C. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.28.478131 · Cited by 13 · see citing works

ST-SCSR: identifying spatial domains in spatial transcriptomics data via structure correlation and self-representation

Zhang, M., Zhang, W., & Ma, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae437 · Cited by 11 · see citing works

Inferring Cell–Cell Communications from Spatially Resolved Transcriptomics Data Using a Bayesian Tweedie Model

Wu, D., Gaskins, J., Sekula, M. N., & Datta, S. (2023) · Genes · doi:10.3390/genes14071368 · Cited by 8 · see citing works

Spatial resolved transcriptomics: Computational insights into gene transcription across tissue and organ architecture in diverse applications

Miao, Z., Tian, T., Chen, W., Wang, Q., Ma, L., Zhang, D., Xie, M., Yu, Z., Guo, X., Bai, G., et al. (2024) · The Innovation Life · doi:10.59717/j.xinn-life.2024.100097 · Cited by 9 · see citing works

Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data

Liu, X., & Ren, X. (2024) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzae057 · Cited by 7 · see citing works

Spatial transcriptomics meets diabetic kidney disease: Illuminating the path to precision medicine

Liu, D., Hu, H., Li, F.-F., Hu, Q., Liu, M., Hao, Y., & Li, B. (2025) · World Journal of Diabetes · doi:10.4239/wjd.v16.i9.107663 · Cited by 7 · see citing works

Identification of Spatial Domains, Spatially Variable Genes, and Genetic Association Studies of Alzheimer Disease with an Autoencoder-based Fuzzy Clustering Algorithm

Cui, Y., Wei, L., Wang, R., Ye, X., & Sakurai, T. (2024) · Current Bioinformatics · doi:10.2174/0115748936278884240102094058 · Cited by 6 · see citing works

Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach

Zhang, M., Liu, Y., Parker, J., An, L., & Sun, X. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06054-y · Cited by 7 · see citing works

Error-Robust and Label-Efficient Deep Learning for Understanding Tumor Microenvironment From Spatial Transcriptomics

Leng, J., Zhang, Y., Liu, X., Cui, Y., Zhao, J., & Ge, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3301677 · Cited by 6 · see citing works

spEMO: Leveraging Multi-Modal Foundation Models for Analyzing Spatial Multi-Omic and Histopathology DataPreprint

Liu, T., Huang, T., Ding, T., Wu, H., Humphrey, P., Perincheri, S., Schalper, K., Ying, R., Xu, H., Zou, J., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.13.632818 · Cited by 6 · see citing works

The intra-tumoral heterogeneity in glioblastoma — a limitation for prognostic value of epigenetic markers?

Sippl, C., Alicia, S., Teping, F., Vanessa, T., Ketter, R., Jin, K. Y., Linsler, S., & Oertel, J. (2023) · Acta Neurochirurgica · doi:10.1007/s00701-023-05594-7 · Cited by 4 · see citing works

Deep Learning Approaches to Enhance Lung Cancer Diagnosis Using Next Generation Sequencing: State of the Art

Shah, S. N. A., Aalam, J., & Parveen, R. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10357-x · Cited by 3 · see citing works

Harnessing spatial transcriptomics to understand host-parasite interactions in plants and animals

Pijnacker, A., Bruggeman, C. W., Korswagen, H. C., Smant, G., & Lozano‐Torres, J. L. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108760 · Cited by 3 · see citing works

Innovation in next-generation sequencing in non-small cell lung cancer diagnostics

Scimone, C., Palumbo, L., Borea, R., Sarracino, C., Tomaiuolo, I., Giovanni, D. D., Alfano, S., Nacchio, M., Russo, G., Russo, A., et al. (2025) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2025.2549538 · Cited by 2 · see citing works

Spatial transcriptomics and multi-omics approach to decipher age-related tissue microenvironments and therapeutics in neurodegeneration and aging

Lakra, A., Singh, N., Vashishth, S., Ambasta, R. K., & Kumar, P. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103189 · Cited by 3 · see citing works

N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene IdentificationPreprint

Heydari, A. A., Davalos, O. A., Hoyer, K. K., & Sindi, S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.12.491682 · Cited by 2 · see citing works

Boosting Single-Cell RNA Sequencing Analysis with Simple Neural AttentionPreprint

Davalos, O. A., Heydari, A. A., Fertig, E. J., Sindi, S., & Hoyer, K. K. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.29.542760 · Cited by 2 · see citing works

Artificial intelligence and deep learning in single-cell omics data analysis: A case study

Anjum, Z., Khalid, W., Takkar, G., Chhetri, P., & Raza, K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27523-4.00007-x · Cited by 1 · see citing works

TissuStamp: a novel high-throughput tissue transfer workflow for FFPE spatial biology assays

Abadia, A. V., Benedetto, G., Hong, Z., Koh, J., Duggal, S., Kovacs, S., Varnosfaderani, M. V., Kalaj, M., Jeffrey-Coker, B., Facchini, A., et al. (2026) · Journal of Histotechnology · doi:10.1080/01478885.2026.2648709 · Cited by 1 · see citing works

Advances in generative biology and bio-inspired artificial intelligence

Wen-Ye, Z. E. N. G., ZHENG, S.-Y., Xiao-Qin, Z. H. A. O., Zhu, W. A. N. G., ZHANG, Y.-J., & Zheng-Yuan, M. A. (2026) · Shengming kexue · doi:10.3724/cbls.2026038 · Cited by 1 · see citing works

The Use of Deep Learning in RNA Therapeutic Development

Subramanian, D. A., Yao, S. L., Chan, A., Witten, J. J., Reker, D., Anderson, D. G., Langer, R., & Traverso, G. (2026) · ACS Nano · doi:10.1021/acsnano.6c02090 · Cited by 1 · see citing works

Deep learning insights into transcriptomics and gene expression patterns analysis

Rai, T., Saji, A., Bisa, S., Vasudevan, K., Rambabu, M., & Dasegowda, K. R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27523-4.00016-0 · Cited by 1 · see citing works

Tools and Databases in Transcriptomics Analysis: Recent Knowledge and Advancements

Mishra, S. K., Roy, S., Chhetri, T., Chandran, K. P., & Georrge, J. J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00067-1

The revolutionary impact of deep learning in transcriptomics and gene expression analysis: A genomic paradigm shift

Ranjan, M. K., Sattar, A. M., Saxena, S., Tiwari, S., & Tiwari, S. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27574-6.00018-7

Bioinformatics and Computational Biology: Tools for the Genomic Era

Saad, M. H. K., Hadi, M. M., Zaghir, A. H., Zidane, Z. M., & Sadk, M. A. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.366

Technological Innovations and Applications of Spatial Transcriptomics in Livestock and Poultry Research

Wang, M., Guo, A., Zhang, L., Wu, S., Du, J., & Yu, T. (2025) · Animal Research and One Health · doi:10.1002/aro2.70016

Autoencoder-aided graph convolutional networks integrating multi-view and multi-scale for improving spatial domain identification

Wang, J., Liang, X., Yuan, S., Liu, J., & Shang, J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114989

Spatial Multi-Omics in Drug Discovery

Ali, K. A., Mandal, D., Dutta, S., Haque, S. N., & Sahana, B. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-4849-0.ch003

MuSACL: Spatially-Adaptive Contrastive Learning with Histological Image Integration for Spatial Transcriptomics Analysis

Wang, Y., Wang, M., Zhang, P., Yang, B., Cheng, Y., & Li, X. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356139

An Effective Approach to Recover Tan Images in Tangram Puzzle Using Novel Generative Adversarial Networks Against Variational Autoencoder Based on Accuracy

Keerthana, P., & Devi., T. (2025) · International Conference on Science Technology Engineering and Mathematics (ICONSTEM) · doi:10.1109/iconstem65670.2025.11374665

Artificial intelligence in spatial transcriptomics: a review of methods, datasets, and directions

Sharma, N., Şen, R., & Erdem, C. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag569

Spatial transcriptomics data and analytical methods: An updated perspective

Danishuddin, M., Khan, S., & Kim, J.-J. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103889 · Cited by 33 · see citing works

SPACEL: deep learning-based characterization of spatial transcriptome architectures

Xu, H., Wang, S., Fang, M., Luo, S., Chen, C., Wan, S., Wang, R., Tang, M., Xue, T., Li, B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43220-3 · Cited by 130 · see citing works

SPACEL: characterizing spatial transcriptome architectures by deep-learningPreprint

Xu, H., Lin, J., Wang, S., Fang, M., Luo, S., Chen, C., Wan, S., Wang, R., Tang, M., Xue, T., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2411530/v1 · Cited by 5 · see citing works

An Integrated and Robust Deep Learning Framework for Denoising and Analyzing Single‐Cell Spatial Transcriptomics

Cui, Y., Cui, Y., Wang, R., Zeng, X., Zhu, Z., Yi, D., Guo, R., Sakurai, T., Nakai, K., Ye, X., et al. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.70429

DTractor enhances cell type deconvolution in spatial transcriptomics by integrating deep neural networks, transfer learning, and matrix factorizationPreprint

Kweon, Y. J., Liu, C., Fonseca, G. J., & Ding, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.12.648541