Evaluating the Utilities of Foundation Models in Single-cell Data Analysis

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

Integrated analysis of multimodal single-cell data

Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works

Comprehensive Integration of Single-Cell Data

Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works

Fast, sensitive and accurate integration of single-cell data with Harmony

Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works

UMAP: Uniform Manifold Approximation and Projection

McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · see citing works

In situ click chemistry generation of cyclooxygenase-2 inhibitors

Bhardwaj, A., Kaur, J., Wuest, M., & Wuest, F. (2017) · Nature Communications · doi:10.1038/s41467-016-0009-6 · Cited by 7,448 · see citing works

Massively parallel digital transcriptional profiling of single cells

Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · 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

Improved training of Wasserstein GANs

Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800 · 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

An overview of gradient descent optimization algorithms

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

LLaMA: Open and Efficient Foundation Language Models

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602 · see citing works

Large language models encode clinical knowledge

Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083 · see citing works

Large language models in medicine

Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944 · see citing works

Performance of neural network basecalling tools for Oxford Nanopore sequencing

Wick, R. R., Judd, L. M., & Holt, K. E. (2019) · Genome Biology · doi:10.1186/s13059-019-1727-y · Cited by 3,221 · see citing works

Simultaneous epitope and transcriptome measurement in single cells

Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works

A Survey on Evaluation of Large Language Models

Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641289 · Cited by 2,842 · see citing works

The Reactome Pathway Knowledgebase

Fabregat, A., Jupe, S., Matthews, L. R., Sidiropoulos, K., Gillespie, M. E., Garapati, P. V., Haw, R., Jassal, B., Korninger, F., May, B., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1132 · Cited by 2,798 · see citing works

PaLM: Scaling Language Modeling with Pathways

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works

Single-cell transcriptomic analysis of Alzheimer’s disease

Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019) · Nature · doi:10.1038/s41586-019-1195-2 · Cited by 2,568 · 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

Opportunities and challenges in long-read sequencing data analysis

Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020) · Genome Biology · doi:10.1186/s13059-020-1935-5 · Cited by 2,530 · see citing works

Attention is not Explanation

Jain, S., & Wallace, B. C. (2019) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n19-1357 · Cited by 2,287 · see citing works

Tackling the widespread and critical impact of batch effects in high-throughput data

Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., Baggerly, K., & Irizarry, R. A. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2825 · Cited by 2,196 · see citing works

A Survey of Large Language Models

Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.18223 · Cited by 2,090

<scp>PANTHER</scp> : Making genome‐scale phylogenetics accessible to all

Thomas, P. D., Ebert, D., Muruganujan, A., Mushayahama, T., Albou, L., & Mi, H. (2022) · Protein Science · doi:10.1002/pro.4218 · Cited by 2,010 · see citing works

Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens

Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · 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

Single-cell chromatin state analysis with Signac

Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., & Satija, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01282-5 · Cited by 1,769 · see citing works

Scaling Laws for Neural Language Models

Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · Cited by 1,749 · see citing works

Cells of the adult human heart

Litviňuková, M., Talavera-López, C., Maatz, H., Reichart, D., Worth, C. L., Lindberg, E. L., Kanda, M., Polanski, K., Heinig, M., Lee, M., et al. (2020) · Nature · doi:10.1038/s41586-020-2797-4 · Cited by 1,729 · see citing works

A comparison of single-cell trajectory inference methods

Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670 · see citing works

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.2 · Cited by 1,586 · see citing works

PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data

Franzén, O., Gan, L.-M., & Björkegren, J. L. M. (2019) · Database · doi:10.1093/database/baz046 · Cited by 1,551 · see citing works

RNA virus interference via CRISPR/Cas13a system in plants

Aman, R., Ali, Z., Butt, H., Mahas, A., Aljedaani, F., Khan, M. Z., Ding, S., & Mahfouz, M. (2018) · Genome Biology · doi:10.1186/s13059-017-1381-1 · Cited by 1,497 · see citing works

Tutorial on Variational Autoencoders

Doersch, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05908 · Cited by 1,470 · see citing works

Benchmarking atlas-level data integration in single-cell genomics

Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works

scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI

Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., & Wang, B. (2023) · doi:10.1101/2023.04.30.538439 · Cited by 1,383 · see citing works

Effective gene expression prediction from sequence by integrating long-range interactions

Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021) · Nature Methods · doi:10.1038/s41592-021-01252-x · Cited by 1,326 · 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

Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing

Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · see citing works

Transfer learning enables predictions in network biology

Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193 · see citing works

Parameter-efficient fine-tuning of large-scale pre-trained language models

Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00626-4 · Cited by 1,191 · see citing works

A rapid and robust method for single cell chromatin accessibility profiling

Chen, X., Miragaia, R. J., Natarajan, K. N., & Teichmann, S. A. (2018) · Nature Communications · doi:10.1038/s41467-018-07771-0 · Cited by 1,147 · see citing works

Method of the Year: spatially resolved transcriptomics

Marx, V. (2021) · Nature Methods · doi:10.1038/s41592-020-01033-y · Cited by 1,132 · see citing works

Squidpy: a scalable framework for spatial omics analysis

Palla, G., Spitzer, H., Klein, M., Fischer, D. S., Schaar, A. C., Kuemmerle, L. B., Rybakov, S., Ibarra, I. L., Holmberg, O., Virshup, I., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01358-2 · Cited by 1,125 · see citing works

Finetuned Language Models Are Zero-Shot Learners

Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101 · see citing works

Construction of a human cell landscape at single-cell level

Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al. (2020) · Nature · doi:10.1038/s41586-020-2157-4 · Cited by 1,087 · see citing works

Deep reinforcement learning from human preferences

Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S., & Amodei, D. (2017) · arXiv · doi:10.48550/arxiv.1706.03741 · Cited by 1,058 · see citing works

Best practices for single-cell analysis across modalities

Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · cited 1× · see citing works

Word2Vec

Church, K. (2016) · Natural Language Engineering · doi:10.1017/s1351324916000334 · Cited by 982 · see citing works

Attention is not not Explanation

Wiegreffe, S., & Pinter, Y. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1002 · Cited by 966 · 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

Splatter: simulation of single-cell RNA sequencing data

Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905 · see citing works

The single-cell pathology landscape of breast cancer

Jackson, H. W., Fischer, J. R., Zanotelli, V. R., Ali, H. R., Mechera, R., Soysal, S. D., Moch, H., Muenst, S. E., Varga, Z., Weber, W. P., et al. (2020) · Nature · doi:10.1038/s41586-019-1876-x · Cited by 891 · see citing works

An integrated cell atlas of the lung in health and disease

Sikkema, L., Ramírez-Suástegui, C., Strobl, D. C., Gillett, T. E., Zappia, L., Madissoon, E., Markov, N. S., Zaragosi, L., Ji, Y., Ansari, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02327-2 · Cited by 857 · see citing works

Single-cell RNA-seq: advances and future challenges

Saliba, A.-E., Westermann, A. J., Gorski, S. A., & Vogel, J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku555 · Cited by 847 · see citing works

Gene regulatory network inference: Data integration in dynamic models—A review

Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755 · 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

Ensembl 2025

Dyer, S. C., Austine-Orimoloye, O., Azov, A. G., Barba, M., Barnes, I., Enriquez, V. P. B., Becker, A., Bennett, R., Beracochea, M., Berry, A. E., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1071 · Cited by 731 · see citing works

Rethinking Attention with Performers

Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.14794 · Cited by 695 · see citing works

Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691 · see citing works

Mish: A Self Regularized Non-Monotonic Activation Function

Misra, D. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.191 · Cited by 683 · see citing works

An accurate and robust imputation method scImpute for single-cell RNA-seq data

Li, W. V., & Li, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03405-7 · Cited by 682 · see citing works

A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.45 · Cited by 682 · see citing works

A comparison of automatic cell identification methods for single-cell RNA sequencing data

Abdelaal, T., Michielsen, L., Cats, D., Hoogduin, D., Mei, H., Reinders, M. J. T., & Mahfouz, A. (2019) · Genome Biology · doi:10.1186/s13059-019-1795-z · Cited by 628 · see citing works

Large-scale foundation model on single-cell transcriptomics

Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628 · see citing works

scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data

Yang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., & Yao, J. (2021) · doi:10.1101/2021.12.05.471261 · Cited by 617 · see citing works

Multi-omics single-cell data integration and regulatory inference with graph-linked embedding

Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616 · see citing works

PaLM-E: An Embodied Multimodal Language Model

Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03378 · Cited by 591 · see citing works

Advances, challenges and opportunities in creating data for trustworthy AI

Liang, W., Tadesse, G. A., Ho, D. E., Fei-Fei, L., Zaharia, M., Zhang, C., & Zou, J. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00516-1 · Cited by 582 · see citing works

Single-cell transcriptomics to explore the immune system in health and disease

Stubbington, M. J. T., Rozenblatt–Rosen, O., Regev, A., & Teichmann, S. A. (2017) · Science · doi:10.1126/science.aan6828 · Cited by 560 · see citing works

LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language ModelsPreprint

Hu, Z., Wang, L., Lan, Y.-H., Xu, W., Lim, E.-P., Bing, L., Xu, X., Poria, S., & Lee, R. K.-W. (2023) · Conference on Empirical Methods in Natural Language Processing · doi:10.48550/arxiv.2304.01933 · Cited by 547 · see citing works

Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex

Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J. R., Sandoval, J. P., et al. (2017) · Science · doi:10.1126/science.aan3351 · Cited by 545 · see citing works

Mish: A Self Regularized Non-Monotonic Activation Function

Misra, D. (2019) · arXiv · doi:10.48550/arxiv.1908.08681 · Cited by 541 · see citing works

A test metric for assessing single-cell RNA-seq batch correction

Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-018-0254-1 · Cited by 511 · see citing works

How is ChatGPT's behavior changing over time?

Chen, L., Zaharia, M., & Zou, J. (2023) · arXiv · doi:10.48550/arxiv.2307.09009 · Cited by 507 · see citing works

How Is ChatGPT’s Behavior Changing Over Time?

Chen, L., Zaharia, M., & Zou, J. (2024) · Harvard Data Science Review · doi:10.1162/99608f92.5317da47 · Cited by 507 · see citing works

Supervised classification enables rapid annotation of cell atlases

Pliner, H. A., Shendure, J., & Trapnell, C. (2019) · doi:10.1101/538652 · Cited by 498 · see citing works

Gene regulatory network inference in the era of single-cell multi-omics

Badia-i-Mompel, P., Wessels, L., Müller‐Dott, S., Trimbour, R., Flores, R. O. R., Argelaguet, R., & Sáez-Rodríguez, J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00618-5 · Cited by 475 · see citing works

Autoencoder for words

Liou, C., Cheng, W., Liou, J.-W., & Liou, D.-R. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.09.055 · Cited by 473 · see citing works

Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney

Muto, Y., Wilson, P. C., Ledru, N., Wu, H., Dimke, H., Waikar, S. S., & Humphreys, B. D. (2021) · Nature Communications · doi:10.1038/s41467-021-22368-w · Cited by 460 · see citing works

Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution

Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01480-9 · Cited by 459 · see citing works

Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes

Rheaume, B. A., Jereen, A., Bolisetty, M. T., Sajid, M. S., Yang, Y. J., Renna, K., Sun, L., Robson, P., & Trakhtenberg, E. F. (2018) · Nature Communications · doi:10.1038/s41467-018-05134-3 · Cited by 401 · see citing works

A comprehensive single cell transcriptional landscape of human hematopoietic progenitors

Pellin, D., Loperfido, M., Baricordi, C., Wolock, S. L., Montepeloso, A., Weinberg, O. K., Biffi, A., Klein, A. M., & Biasco, L. (2019) · Nature Communications · doi:10.1038/s41467-019-10291-0 · Cited by 360 · see citing works

Predicting transcriptional outcomes of novel multigene perturbations with GEARS

Roohani, Y., Huang, K., & Leskovec, J. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01905-6 · Cited by 355 · see citing works

LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models

Hu, Z., Wang, L., Lan, Y., Xu, W., Lim, E., Bing, L., Xu, X., Poria, S., & Lee, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.319 · Cited by 349 · see citing works

Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-trainingPreprint

Liu, H., Li, Z., Hall, D. L. W., et al. (2023) · International Conference on Learning Representations · doi:10.48550/arxiv.2305.14342 · Cited by 331 · see citing works

Predicting cellular responses to complex perturbations in high‐throughput screens

Lotfollahi, M., Klimovskaia Susmelj, A., De Donno, C., Hetzel, L., Ji, Y., Ibarra, I. L., Srivatsan, S. R., Naghipourfar, M., Daza, R. M., Martin, B., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202211517 · Cited by 307 · see citing works

Symbolic Discovery of Optimization Algorithms

Chen, X., Liang, C., Huang, D., Real, E., Wang, K., Liu, Y., Pham, H., Dong, X., Luong, T., Hsieh, C.-J., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.06675 · Cited by 303 · see citing works

Highly scalable generation of DNA methylation profiles in single cells

Mulqueen, R. M., Pokholok, D., Norberg, S. J., Torkenczy, K. A., Fields, A. J., Sun, D., Sinnamon, J. R., Shendure, J., Trapnell, C., O'Roak, B. J., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4112 · Cited by 289 · see citing works

Systems immunology: just getting started

Davis, M. M., Tato, C. M., & Furman, D. (2017) · Nature Immunology · doi:10.1038/ni.3768 · Cited by 273 · see citing works

A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics

Li, H., Zhou, J., Li, Z., Chen, S., Liao, X., Zhang, B., Zhang, R., Wang, Y., Sun, S., & Gao, X. (2023) · Nature Communications · doi:10.1038/s41467-023-37168-7 · Cited by 240 · see citing works

A Review on Word Embedding Techniques for Text Classification

Birunda, S. S., & Devi, R. K. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-9651-3_23 · Cited by 224 · see citing works

MathPrompter: Mathematical Reasoning using Large Language Models

Imani, S., Du, L., & Shrivastava, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.4 · Cited by 212 · see citing works

Open questions: How many genes do we have?

Salzberg, S. L. (2018) · BMC Biology · doi:10.1186/s12915-018-0564-x · Cited by 211 · see citing works

Gene2Vec: Distributed Representation of Genes Based on Co-Expression

Du, J., Jia, P., Dai, Y., Tao, C., Zhao, Z., & Zhi, D. (2018) · doi:10.1101/286096 · Cited by 204 · see citing works

Are Emergent Abilities of Large Language Models a Mirage?

Schaeffer, R., Miranda, B., & Koyejo, S. (2023) · arXiv · doi:10.48550/arxiv.2304.15004 · Cited by 200 · see citing works

Transformer for one stop interpretable cell type annotation

Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189 · see citing works

Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows

Salas, S. M., Kuemmerle, L. B., Langseth, C. M., Tiesmeyer, S., Avenel, C., Hu, T., Rehman, H., Grillo, M., Czarnewski, P., Helgadóttir, S., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02617-2 · Cited by 185 · see citing works

A genomic perspective on HLA evolution

Meyer, D., Aguiar, V. R. C., Bitarello, B. D., Brandt, D. Y. C., & Nunes, K. (2017) · Immunogenetics · doi:10.1007/s00251-017-1017-3 · Cited by 182 · see citing works

A cell atlas foundation model for scalable search of similar human cells

Heimberg, G., Kuo, T. C.-Y., DePianto, D. J., Salem, O., Heigl, T., Diamant, N., Scalia, G., Biancalani, T., Turley, S. J., Rock, J. R., et al. (2024) · Nature · doi:10.1038/s41586-024-08411-y · Cited by 179 · see citing works

Mapping Bias Overestimates Reference Allele Frequencies at the HLA Genes in the 1000 Genomes Project Phase I Data

Brandt, D. Y. C., Aguiar, V. R. C., Bitarello, B. D., Nunes, K., Goudet, J., & Meyer, D. (2015) · G3 Genes Genomes Genetics · doi:10.1534/g3.114.015784 · Cited by 177 · see citing works

Travelling the world of gene-gene interactions

Steen, K. V. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr012 · Cited by 176 · see citing works

BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies

Li, Z., & Zhou, X. (2022) · Genome biology · doi:10.1186/s13059-022-02734-7 · Cited by 173 · see citing works

scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning

Lin, Y., Wu, T.-Y., Wan, S., Yang, J. Y. H., Wong, W. H., & Wang, Y. X. R. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01161-6 · Cited by 166 · see citing works

Universal Cell Embeddings: A Foundation Model for Cell BiologyPreprint

Rosen, Y., Roohani, Y., Agrawal, A., Samotorcan, L., Quake, S. R., & Leskovec, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.28.568918 · Cited by 165 · see citing works

scClassify: sample size estimation and multiscale classification of cells using single and multiple reference

Lin, Y., Cao, Y., Kim, H. J., Salim, A., Speed, T. P., Lin, D., Yang, P., & Yang, J. Y. H. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199389 · Cited by 164 · see citing works

Evaluation of Cell Type Annotation R Packages on Single-Cell RNA-Seq Data

Huang, Q., Liu, Y., Du, Y., & Garmire, L. X. (2021) · Genomics, Proteomics &amp; Bioinformatics · doi:10.1016/j.gpb.2020.07.004 · Cited by 149 · see citing works

scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics

Song, D., Wang, Q., Yan, G., Liu, T., Sun, T., & Li, J. J. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01772-1 · Cited by 148 · see citing works

GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model

Yang, X., Liu, G., Feng, G., Bu, D., Wang, P., Jiang, J., Chen, S., Yang, Q., Miao, H., Zhang, Y., et al. (2024) · Cell Research · doi:10.1038/s41422-024-01034-y · Cited by 148 · see citing works

Method of the Year 2019: Single-cell multimodal omics

(2020) · Nature Methods · doi:10.1038/s41592-019-0703-5 · Cited by 141 · see citing works

Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning

Xu, R., Luo, F., Zhang, Z., Tan, C., Chang, B., Huang, S., & Huang, F. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.749 · Cited by 137 · see citing works

Network inference with Granger causality ensembles on single-cell transcriptomics

Deshpande, A., Chu, L.-F., Stewart, R., & Gitter, A. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110333 · Cited by 130 · see citing works

Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

Rosen, Y., Brbić, M., Roohani, Y., Swanson, K., Li, Z., & Leskovec, J. (2024) · Nature Methods · doi:10.1038/s41592-024-02191-z · Cited by 125 · see citing works

A comparison of marker gene selection methods for single-cell RNA sequencing data

Pullin, J. M., & McCarthy, D. J. (2022) · doi:10.1101/2022.05.09.490241 · Cited by 122 · see citing works

Applications of Single-Cell DNA Sequencing

Evrony, G. D., Hinch, A. G., & Luo, C. (2021) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-111320-090436 · Cited by 120 · see citing works

Benchmarking strategies for cross-species integration of single-cell RNA sequencing data

Song, Y., Miao, Z., Brāzma, A., & Papatheodorou, I. (2023) · Nature Communications · doi:10.1038/s41467-023-41855-w · Cited by 106 · see citing works

GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPTPreprint

Chen, Y. T., & Zou, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.16.562533 · Cited by 106 · see citing works

CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells

Zeng, Y., Xie, J., Shangguan, N., Wei, Z., Li, W., Su, Y., Yang, S., Zhang, C., Zhang, J., Fang, N., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59926-5 · Cited by 93 · see citing works

CellPLM: Pre-training of Cell Language Model Beyond Single CellsPreprint

Wen, H., Tang, W., Dai, X., Ding, J., Jin, W., Xie, Y., & Tang, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.03.560734 · Cited by 85 · see citing works

Is Attention Explanation? An Introduction to the Debate

Bibal, A., Cardon, R., Alfter, D., Wilkens, R., Wang, X., François, T., & Watrin, P. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.269 · Cited by 83 · see citing works

Cell-type-specific co-expression inference from single cell RNA-sequencing data

Su, C., Xu, Z., Shan, X., Cai, B., Zhao, H., & Zhang, J. (2023) · Nature Communications · doi:10.1038/s41467-023-40503-7 · Cited by 80 · see citing works

Generative pretraining from large-scale transcriptomes for single-cell deciphering

Shen, H., Liu, J., Hu, J., Shen, X., Zhang, C., Wu, D., Feng, M., Yang, M., Li, Y., Yang, Y., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106536 · Cited by 73 · see citing works

Contrastive Learning Reduces Hallucination in Conversations

Sun, W., Shi, Z., Gao, S., Ren, P., De Rijke, M., & Ren, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26596 · Cited by 69 · see citing works

ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre IdentificationPreprint

Kuzman, T., Mozetič, I., & Ljubešić, N. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.03953 · Cited by 65 · see citing works

Assessing evolutionary and developmental transcriptome dynamics in homologous cell types

Feregrino, C., & Tschopp, P. (2021) · Developmental Dynamics · doi:10.1002/dvdy.384 · Cited by 51 · see citing works

Novae: a graph-based foundation model for spatial transcriptomics data

Blampey, Q., Benkirane, H., Bercovici, N., Mulder, K., Gessain, G., Ginhoux, F., André, F., & Cournède, P.-H. (2025) · Nature Methods · doi:10.1038/s41592-025-02899-6 · Cited by 50 · see citing works

Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution

Kang, J. B., Shen, A. Z., Gurajala, S., Nathan, A., Rumker, L., Aguiar, V. R. C., Valencia, C., Lagattuta, K. A., Zhang, F., Jonsson, A. H., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01586-6 · Cited by 49 · see citing works

Grounding Large Language Models in Interactive Environments with Online Reinforcement LearningPreprint

Carta, T., Romac, C., Wolf, T., Lamprier, S., Sigaud, O., & Oudeyer, P. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2302.02662 · Cited by 48 · see citing works

Disease state prediction from single-cell data using graph attention networks

Ravindra, N. G., Sehanobish, A., Pappalardo, J. L., Hafler, D. A., & van Dijk, D. (2020) · ACM Conference on Health, Inference, and Learning (CHIL) · doi:10.1145/3368555.3384449 · Cited by 47 · see citing works

Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease

Sakaue, S., Gurajala, S., Curtis, M. L., Luo, Y., Choi, W., Ishigaki, K., Kang, J. B., Rumker, L., Deutsch, A. J., Schönherr, S., et al. (2023) · Nature Protocols · doi:10.1038/s41596-023-00853-4 · Cited by 46 · see citing works

GEARS: Predicting transcriptional outcomes of novel multi-gene perturbationsPreprint

Roohani, Y., Huang, K., & Leskovec, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.12.499735 · Cited by 40 · see citing works

Single-Cell Multiomics

Flynn, E., Almonte-Loya, A., & Fragiadakis, G. K. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020422-050645 · Cited by 40 · see citing works

Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages

Heimberg, G., Kuo, T., DePianto, D., Heigl, T., Diamant, N., Salem, O., Scalia, G., Biancalani, T., Turley, S., Rock, J., et al. (2023) · doi:10.1101/2023.07.18.549537 · Cited by 40 · see citing works

Transformer-Based Single-Cell Language Model: A Survey

Lan, W., He, G., Liu, M., Chen, Q., Cao, J., & Peng, W. (2024) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020034 · Cited by 38 · see citing works

When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning MethodPreprint

Zhang, B., Liu, Z., Cherry, C. A., & Fırat, O. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.17193 · Cited by 38 · see citing works

Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive LearningPreprint

Zhao, S.-Y., Zhang, J., & Nie, Z.-Q. (2023) · arXiv.org · doi:10.48550/arxiv.2306.04371 · Cited by 37 · see citing works

Automatic genre identification: a survey

Kuzman, T., & Ljubešić, N. (2023) · Language Resources and Evaluation · doi:10.1007/s10579-023-09695-8 · Cited by 34 · see citing works

Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuningPreprint

Xu, R., Luo, F., Zhang, Z., Tan, C., Chang, B., Huang, S., & Huang, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.05687 · Cited by 33 · see citing works

STPath: a generative foundation model for integrating spatial transcriptomics and whole-slide images

Huang, T., Liu, T., Babadi, M., Ying, R., & Jin, W. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02020-3 · Cited by 28 · see citing works

ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks

Wang, Y., Liu, T., & Zhao, H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac427 · Cited by 25 · see citing works

Modelling Immune System: Principles, Models, Analysis and Perspectives

Li, X., Wang, Z., Lu, T., & Che, X. (2009) · Journal of Bionic Engineering · doi:10.1016/s1672-6529(08)60101-8 · Cited by 25 · see citing works

Multilingual translation for zero-shot biomedical classification using BioTranslator

Xu, H., Woicik, A., Poon, H., Altman, R. B., & Wang, S. (2023) · Nature Communications · doi:10.1038/s41467-023-36476-2 · Cited by 24 · see citing works

To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis

Xue, F., Fu, Y., Zhou, W., Zheng, Z., & You, Y. (2023) · arXiv · doi:10.48550/arxiv.2305.13230 · Cited by 22 · see citing works

Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type, and sex specificity of gene expression with novel genetic risk for MERTK in female

Zhang, L., Liu, T., He, C. H., Russo, A., Coffey, S., Yin, D., Hsu, I.-U., Zhang, C., Ye, Y., Su, C., et al. (2026) · Journal of Alzheimer’s Disease · doi:10.1177/13872877261480719 · Cited by 22 · see citing works

To Repeat or Not To Repeat: Insights from Scaling LLM under Token-Crisis

Xue, F., Fu, Y., Zhou, W., Zheng, Z., & You, Y. (2023) · neural information processing systems · doi:10.52202/075280-2590 · Cited by 22 · see citing works

Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use CasesPreprint

Ji, Y., Deng, Y., Gong, Y., Peng, Y., Niu, Q., Lei Zhang, A., Ma, B., & Li, X. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.14742 · Cited by 21 · see citing works

Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks

Wu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2023) · arXiv · doi:10.48550/arxiv.2307.02477 · Cited by 19 · see citing works

A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer’s disease network

Sebastian, S., Roy, S., & Kalita, J. K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac482 · Cited by 12 · see citing works

MyGene.info: gene annotation query as a servicePreprint

Wu, C., Mark, A., & Su, A. I. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/009332 · Cited by 12 · see citing works

A statistical genetics guide to identifying HLA alleles driving complex diseasePreprint

Sakaue, S., Gurajala, S., Curtis, M. L., Luo, Y., Choi, W., Ishigaki, K., Kang, J. B., Rumker, L., Deutsch, A. J., Schönherr, S., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.24.504550 · Cited by 6 · see citing works

Transformers in single-cell omics: a review and new perspectives

Szałata, A., Hrovatin, K., Becker, S., Tejada-Lapuerta, A., Cui, H., Wang, B., & Theis, F. J. (2024) · Nature Methods · doi:10.1038/s41592-024-02353-z · Cited by 176 · see citing works

Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines

Ahlmann-Eltze, C., Huber, W., & Anders, S. (2025) · Nature Methods · doi:10.1038/s41592-025-02772-6 · Cited by 238 · see citing works

Foundation models in bioinformatics

Guo, F., Guan, R., Li, Y., Liu, Q., Wang, X., Yang, C., & Wang, J. (2025) · National Science Review · doi:10.1093/nsr/nwaf028 · Cited by 121 · see citing works

CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells

Zeng, Y., Xie, J., Shangguan, N., Wei, Z., Li, W., Su, Y., Yang, S., Zhang, C., Zhang, J., Fang, N., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59926-5 · Cited by 93 · see citing works

Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear baselines

Ahlmann-Eltze, C., Huber, W., & Anders, S. (2024) · doi:10.1101/2024.09.16.613342 · Cited by 268 · see citing works

Nicheformer: A Foundation Model for Single-Cell and Spatial OmicsPreprint

Schaar, A. C., Tejada-Lapuerta, A., Palla, G., Gutgesell, R. M., Halle, L., Minaeva, M., Vornholz, L., Dony, L., Drummer, F., Bahrami, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.15.589472 · Cited by 66 · see citing works

New horizons at the interface of artificial intelligence and translational cancer research

Yates, J., & Allen, E. M. V. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.03.018 · Cited by 55 · see citing works

Large language models in biomedicine and healthcare

Zhou, J., Li, H., Chen, S., Chen, Z., Han, Z., & Gao, X. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00047-1 · Cited by 61 · see citing works

Benchmarking foundation cell models for post-perturbation RNA-seq prediction

Csendes, G., Sanz, G., Szalay, K. Z., & Szalai, B. (2025) · BMC Genomics · doi:10.1186/s12864-025-11600-2 · Cited by 47 · see citing works

Single-cell foundation models: bringing artificial intelligence into cell biology

Baek, S., Song, K., & Lee, I. (2025) · Experimental &amp; Molecular Medicine · doi:10.1038/s12276-025-01547-5 · Cited by 46 · see citing works

Harnessing the deep learning power of foundation models in single-cell omics

Ma, Q., Jiang, Y., Cheng, H., & Xu, D. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00756-6 · Cited by 34 · see citing works

Embeddings from language models are good learners for single-cell data analysis

Liu, T., Chen, T., Zheng, W., Luo, X., Chen, Y. T., & Zhao, H. (2026) · Patterns · doi:10.1016/j.patter.2025.101431 · Cited by 33 · see citing works

Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations

Wong, D. R., Hill, A. S., & Moccia, R. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf317 · Cited by 27 · see citing works

scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis

Liu, T., Chen, T., Zheng, W., Luo, X., Chen, Y., & Zhao, H. (2023) · doi:10.1101/2023.12.07.569910 · Cited by 30 · see citing works

Systems Human Immunology and AI: Immune Setpoint and Immune Health

Lei, Y., & Tsang, J. S. (2025) · Annual Review of Immunology · doi:10.1146/annurev-immunol-090122-042631 · Cited by 25 · see citing works

Limitations of cell embedding metrics assessed using drifting islands

Wang, H., Leskovec, J., & Regev, A. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02702-z · Cited by 24 · see citing works

Biology-driven insights into the power of single-cell foundation models

Wu, J., Ye, Q., Wang, Y., Hu, R., Zhu, Y., Yin, M., Wang, T., Wang, J., Hsieh, C., & Hou, T. (2025) · Genome biology · doi:10.1186/s13059-025-03781-6 · Cited by 30 · see citing works

Out of distribution learning in bioinformatics: advancements and challenges

Shi, Y., Xu, W., & Hu, P. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf294 · Cited by 21 · see citing works

Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDAPreprint

Wang, G., Liu, T., Zhao, J., Cheng, Y., & Zhao, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.04.626878 · Cited by 24 · see citing works

JOINTLY: interpretable joint clustering of single-cell transcriptomes

Møller, A. F., & Madsen, J. G. S. (2023) · Nature Communications · doi:10.1038/s41467-023-44279-8 · Cited by 15 · see citing works

Scaling Dense Representations for Single Cell with Transcriptome-Scale ContextPreprint

Ho, N., Ellington, C. N., Hou, J., Addagudi, S., Mo, S., Tao, T., Li, D., Zhuang, Y., Wang, H., Cheng, X., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.28.625303 · Cited by 16 · see citing works

A comparison of computational methods for expression forecasting

Kernfeld, E. M., Yang, Y., Weinstock, J. S., Battle, A. J., & Cahan, P. (2025) · Genome biology · doi:10.1186/s13059-025-03840-y · Cited by 36 · see citing works

Stack: In-Context Learning of Single-Cell BiologyPreprint

Dong, M., Adduri, A., Gautam, D., Wu, L., Kernick, C., Coons, M. M., Chih, Y.-C., Carpenter, C., Shah, R. M., Ricci-Tam, C., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.09.698608 · Cited by 20 · see citing works

Leveraging multi-modal foundation models for analysing spatial multi-omic and histopathology data

Liu, T., Huang, T., Ding, T., Wu, H., Humphrey, P., Perincheri, S., Schalper, K., Ying, R., Xu, H., Zou, J., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01602-6 · Cited by 21 · see citing works

Advancing plant single-cell genomics with foundation models

Chau, T. N., Wang, X., McDowell, J. M., & Li, S. (2024) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2024.102666 · Cited by 14 · see citing works

Virtual Cells Need Context, Not Just ScalePreprint

Dibaeinia, P., Babu, S., Knudson, M. K., ElSheikh, A., Wen, Y., Liu, H., Perera, J. F., & Khan, A. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.04.703804 · Cited by 15 · see citing works

CellNavi predicts genes directing cellular transitions by learning a gene graph-enhanced cell state manifold

Wang, T., Pan, Y., Ju, F., Zheng, S., Liu, C., Min, Y., Jiang, Q., Liu, X., Xia, H., Liu, G., et al. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01755-1 · Cited by 9 · see citing works

Parameter-Efficient Fine-Tuning Enhances Adaptation of Single Cell Large Language Model for Cell Type IdentificationPreprint

He, F., Fei, R., Gao, M., Su, L., Zhang, X., & Xu, D. H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.27.577455 · Cited by 7 · see citing works

PathOmCLIP: Connecting tumor histology with spatial gene expression via locally enhanced contrastive learning of Pathology and Single-cell foundation modelPreprint

Lee, Y., Liu, X., Hao, M., Liu, T., & Regev, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.10.627865 · Cited by 7 · see citing works

Towards the Next Generation of Data‐Driven Therapeutics Using Spatially Resolved Single‐Cell Technologies and Generative AI

Rodov, A., Baniadam, H., Zeiser, R., Amit, I., Yosef, N., Wertheimer, T., & Ingelfinger, F. (2025) · European Journal of Immunology · doi:10.1002/eji.202451234 · Cited by 6 · see citing works

Small, open-source text-embedding models as substitutes to OpenAI models for gene analysis

Gan, D., & Li, J. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.07.053 · Cited by 6 · see citing works

SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration

Wang, Y., Fan, Y., Wang, X., Yu, T., Zong, Y., Liu, X., Zhong, G., Liu, M., Li, Q., Lee, K. H., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72570-x · Cited by 9 · 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

Toward informed batch correction for single-cell transcriptome integration

Li, S., Luecken, M. D., Marioni, J. C., Teichmann, S. A., & He, P. (2026) · Nature Computational Science · doi:10.1038/s43588-025-00943-1 · Cited by 7 · see citing works

Unravelling Convergent Signaling Mechanisms Underlying the Aging-Disease Nexus Using Computational Language Analysis

Junyent, M., Noori, H., Schepper, R. D., Frajdenberg, S., Elsaigh, R. K. A. H., McDonald, P. H., Duckett, D. R., & Maudsley, S. R. (2025) · Current Issues in Molecular Biology · doi:10.3390/cimb47030189 · Cited by 5 · see citing works

Sparse Autoencoders Reveal Interpretable Features in Single-Cell Foundation ModelsPreprint

Pedrocchi, F., Barkmann, F., Joudaki, A., & Boeva, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.22.681631 · Cited by 7 · see citing works

SpatialFusion: A lightweight multimodal foundation model for pathway-informed spatial niche mappingPreprint

Yates, J., Shavakhi, M., Choueiri, T. K., Allen, E. M. V., & Uhler, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.16.712056 · Cited by 5 · see citing works

Overcoming barriers to the wide adoption of single-cell large language models in biomedical research

Xie, F., Zhao, B., Xu, S., Wang, Z., Moon, J. J., & Garmire, L. X. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02846-y · Cited by 4 · see citing works

An expanded role for single-cell chemical genomics profiling in drug discovery

Wyatt, A., Hoffer-Hawlik, K., Giglio, R. M., Azizi, E., & McFaline‐Figueroa, J. L. (2026) · Biochemical Journal · doi:10.1042/bcj20253273 · Cited by 3 · see citing works

A perspective on developing foundation models for analyzing spatial transcriptomic data

Liu, T., Hao, M., Liu, X., & Zhao, H. (2025) · Quantitative Biology · doi:10.1002/qub2.70010 · Cited by 3 · see citing works

Evaluating Foundation Models for In-Silico PerturbationPreprint

Liu, X., Boylan, J., Bouiller, T., Solovyeva, E. M., Ataman, B., Hoersch, S., Jenkins, J. L., & Devarakonda, M. V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.11.653338 · Cited by 3 · see citing works

BioLab: End-to-End Autonomous Life Sciences Research with Multi-Agents System Integrating Biological Foundation ModelsPreprint

Jin, R., Guo, Y., Qu, Y., Yang, M., Shang, C., Yang, Q., Chao, L., Zhou, Y., Xu, R. M., Xu, Z., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.03.674085 · Cited by 3 · see citing works

scConcept: Contrastive pretraining for technology-agnostic single-cell representations beyond reconstructionPreprint

Bahrami, M., Tejada-Lapuerta, A., Becker, S., G., F. S. H., & Theis, F. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.14.682419 · Cited by 4 · see citing works

SuperGLUE facilitates an explainable training framework for multi-modal data analysis

Liu, T., Zhao, J., & Zhao, H. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101167 · Cited by 2 · see citing works

Benchmarking biomedical foundation models

Sáez-Rodríguez, J., Schäfer, P. S. L., Kalavros, N., & Stolovitzky, G. A. (2026) · Nature Methods · doi:10.1038/s41592-026-03182-y · Cited by 2 · see citing works

Prediction of outcome from spatial Protein profiling of triple-negative breast cancers

pour, A. F., Wu, T.-C., Noorbakhsh, J., Martínek, J., Sivajothi, S. K., Robson, P., Palucka, K., & Chuang, J. H. (2026) · Communications Medicine · doi:10.1038/s43856-026-01400-4 · Cited by 2 · see citing works

Reply to: Deeper evaluation of a single-cell foundation model

Yang, F., Wang, F., Huang, L.-K., Liu, L., Huang, J., & Yao, J. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00948-x · Cited by 2 · see citing works

HEIMDALL: Disentangling tokenizer design for robust transfer in single-cell foundation modelsPreprint

Haber, E., Alam, S., Ho, N., Liu, R., Trop, E., Liang, S., Yang, M., Krieger, S., & Ma, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.09.687403 · Cited by 2 · see citing works

scPRINT-2: Towards the next-generation of cell foundation models and benchmarksPreprint

Kalfon, J., Gabriel Peyré, A., & Cantini, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.11.693702 · Cited by 2 · see citing works

Evaluating the learnability of single-cell large language models on multiple tasks

Yan, Y., Wang, X., & Song, D. (2026) · BMC Genomics · doi:10.1186/s12864-026-12975-6 · Cited by 1 · see citing works

Trustworthy Agentic AI in Bioinformatics: From Workflow Automation to Traceable and Validated Biological Inference

Ang, M. Y., Lipovich, L., Choo, S. W., Chen, L., & Song, L. (2026) · Biology · doi:10.3390/biology15171537 · Cited by 1 · see citing works

OmicOS: A Comprehensive Omics Ecosystem Infrastructure and Agent System for the AI EraPreprint

Zeng, Z., Meng, X., Hu, L., Li, C., Liu, P., Shi, Y., Ma, X., Gao, L., Wang, X., Luo, Z., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.11.731775 · Cited by 1 · see citing works

Pedoman Peri-operatif Renal Assessment

Sinaga, U. M. (2009)

RobustCell: a model attack-defense framework for robust transcriptomic data analysis

Liu, T., Kang, Q., Luo, X., Luo, X., & Zhao, H. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00799-9

Beyond Integration: SuperGLUE Facilitates Explainable Training Framework for Multi-modal Data AnalysisPreprint

Liu, T., Zhao, J., & Zhao, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.19.624293

scFTAT: a novel cell annotation method integrating FFT and transformer

Tang, B., & Chen, Y. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06061-z

spRefine Denoises and Imputes Spatial Transcriptomics with a Reference-Free Framework Powered by Genomic Language ModelPreprint

Liu, T., Huang, T., Jin, W., Chu, T., Ying, R., & Zhao, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.22.649977

eSPred: Explainable scRNA-seq Prediction via Customized Foundation Models and Pathway-Aware Fine-tuningPreprint

Sun, L., Yang, Q., Zhang, J., Guo, W., & Lin, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.14.654052

BrainBridge Characterizes Key Factors affecting Alzheimer’s Disease and Associated PhenotypesPreprint

Liu, T., Hao, M., Sun, E., Markov, Y., Zhang, L., Zou, J., & Zhao, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.03.692255

Batch Effects Remain a Fundamental Barrier to Universal Embeddings in Single-Cell Foundation ModelsPreprint

Wang, L., Zhang, C., & Zhang, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.19.695371 · Cited by 1 · see citing works

Benchmarking single-cell foundation models for real-world RNA-seq data integrationPreprint

Han, S., Sztanka-Toth, T. R., Senel, E., Elnaggar, A., Patel, J., Mansi, T., Smirnov, D. A., Greshock, J., & Javidi, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.17.719314

Standardizing Benchmarks for Plant Genomic Prediction

Yan, M., Wang, W., Zhang, Y., Guo, H., Xue, Z., Wang, X., & Yan, J. (2026) · Agronomy · doi:10.3390/agronomy16121131

Novel regulatory network of PS-1/β-catenin/p-PTEN axis in gastric cancer invasion

Xiao, S., Song, D.-D., Yu, Y.-P., Xu, X.-Z., Tang, J.-F., & Zhou, C.-F. (2026) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v18.i7.118256

A Systematic Literature Review of Single-Cell Foundation Models for Transcriptomics: Design, Adaptation, Benchmarks, and Future Directions

Zhao, X., Pang, Y. Y., Fu, T. Y., Yang, C. N., & Moi, S. H. (2026) · IEEE Access · doi:10.1109/access.2026.3744403

Foundation Models for Single-cell Biology: A Review of Architectures, Benchmarks, and the Push toward Mechanistic UnderstandingPreprint

Denvis, O. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6847538

Accessible and reproducible deployment reveals the practical boundaries of single-cell foundation modelsPreprint

Hou, S., Yang, P., Ma, W., Xiang, J., Wang, J. X., Wan, H., Ma, Y., & Zhou, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.06.698060 · Cited by 2 · see citing works

Single-cell foundation models: bringing artificial intelligence into cell biology

Baek, S., Song, K., & Lee, I. (2025) · Experimental &amp; Molecular Medicine · doi:10.1038/s12276-025-01547-5 · Cited by 46 · see citing works

Benchmarking single-cell foundation models in a zero-shot settingPreprint

Gaballa, Y., Ahmed, S., & Abdelaal, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.03.739553 · Cited by 1 · see citing works

Fundamental Limitations of Foundation Models in Single-Cell TranscriptomicsPreprint

Atti, S., & Subramaniam, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.26.661767 · Cited by 7 · see citing works