The development and application of deep learning for stem cell research

Zhenfu Li; Wenjing Zhang; Yuxin Wu; Yaxuan Ye; Yuxin Chen; Jian Wu; Di Chen

doi:10.1016/j.jgg.2026.05.008

Turn off MathJax

Article Contents

Article Navigation > Journal of Genetics and Genomics > 2026 > Accepted Manuscript

PDF( 0 KB)

The development and application of deep learning for stem cell research

doi: 10.1016/j.jgg.2026.05.008

Zhenfu Li^a,b,
Wenjing Zhang^a,
Yuxin Wu^a,
Yaxuan Ye^a,
Yuxin Chen^a,
Jian Wu^c,d,
Di Chen^a,b,c,e

a. Center for Reproductive Medicine of the Second Affiliated Hospital, Center for Regeneration and Cell Therapy of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China;
b. Edinburgh Medical School:Biomedical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK;
c. Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence, Zhejiang University, Hangzhou, Zhejiang 310058, China;
d. State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital, School of Medicine and School of Public Health, Zhejiang University, Hangzhou, Zhejiang 310058, China;
e. State Key Laboratory of Biobased Transportation Fuel Technology, Haining, Zhejiang 314400, China

Funds:

This work was supported by the National Natural Science Foundation of China (32270835) and National Key R&

D Program of China (2024YFA1108100).

Received Date: 2026-01-29
Accepted Date: 2026-05-17
Rev Recd Date: 2026-05-12

Available Online: 2026-05-23

Abstract

Abstract

Stem cells reside in specific microenvironments where they divide to maintain themselves and differentiate into functional cells that replace old, dead, or damaged cells to maintain tissue homeostasis. Some stem cells could also be cultured in vitro for self-renewal and directed differentiation towards specific lineages for both mechanistic interrogation and clinic investigation. A deeper understanding of the regulatory mechanisms underlying stem cell properties is essential for advancing their translational applications. Deep learning (DL), a branch of artificial intelligence, has been widely and deeply incorporated into different fields including biology. The application of DL in stem cells has revolutionized our research strategies and provided significant technical advantages. Here, we review the latest advances of the application of DL in analyzing bioimaging data and exploring large-scale genomics data in stem cells. We also summarize the limitations of current DL models and challenges of the application of these models in stem cell research.
- Deep learning,
- Stem cells,
- Stem cell imaging,
- Stem cell omics,
- Regulatory mechanism

FullText(HTML)

References(234)

References

Abbas, A., Chandratre, K., Gao, Y., Yuan, J., Zhang, M.Q., Mani, R.S., 2024. ChIPr: accurate prediction of cohesin-mediated 3D genome organization from 2D chromatin features. Genome Biol. 25, 15.

Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A.J., Bambrick, J., et al., 2024. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493-500.

Adduri, A.K., Gautam, D., Bevilacqua, B., Imran, A., Shah, R., Naghipourfar, M., Teyssier, N., Ilango, R., Nagaraj, S., Dong, M., et al., 2025. Predicting cellular responses to perturbation across diverse contexts with State. bioRxiv. https://doi.org/10.1101/2025.06.26.661135.

Aida, S., Okugawa, J., Fujisaka, S., Kasai, T., Kameda, H., Sugiyama, T., 2020. Deep learning of cancer stem cell morphology using conditional generative adversarial networks. Biomolecules 10, 931.

Alanis-Lobato, G., Bartlett, T.E., Huang, Q., Simon, C.S., McCarthy, A., Elder, K., Snell, P., Christie, L., Niakan, K.K., 2023. MICA: a multi-omics method to predict gene regulatory networks in early human embryos. Life Sci. Alliance 7, e202302415.

Allis, C.D., Jenuwein, T., 2016. The molecular hallmarks of epigenetic control. Nat. Rev. Genet. 17, 487-500.

Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaria, J., Fadhel, M.A., Al-Amidie, M., Farhan, L., 2021. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 53.

Arthur, T.D., Nguyen, J.P., D’Antonio-Chronowska, A., Matsui, H., Silva, N.S., Joshua, I.N., Aguiar, L.R., Arias, A.D., Benaglio, P., Berggren, W.T., et al., 2024. Complex regulatory networks influence pluripotent cell state transitions in human iPSCs. Nat. Commun. 15, 1664.

Ashuach, T., Reidenbach, D.A., Gayoso, A., Yosef, N., 2022. PeakVI: A deep generative model for single-cell chromatin accessibility analysis. Cell Rep. Methods 2.

Avsec, Z., Agarwal, V., Visentin, D., Ledsam, J.R., Grabska-Barwinska, A., Taylor, K.R., Assael, Y., Jumper, J., Kohli, P., Kelley, D.R., 2021. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196-1203.

Badia-i-Mompel, P., Wessels, L., Muller-Dott, S., Trimbour, R., Ramirez Flores, R.O., Argelaguet, R., Saez-Rodriguez, J., 2023. Gene regulatory network inference in the era of single-cell multi-omics. Nat. Rev. Genet. 24, 739-754.

Ballard, J.L., Wang, Z., Li, W., Shen, L., Long, Q., 2024. Deep learning-based approaches for multi-omics data integration and analysis. BioData Min. 17, 38.

Bamford, P., Lovell, B., 1998. Unsupervised cell nucleus segmentation with active contours. Signal Process. 71, 203-213.

Bateman, A., Coin, L., Durbin, R., Finn, R.D., Hollich, V., Griffiths-Jones, S., Khanna, A., Marshall, M., Moxon, S., Sonnhammer, E.L.L., et al., 2004. The Pfam protein families database. Nucleic Acids Res. 32, D138-D141.

Batlle, E., Clevers, H., 2017. Cancer stem cells revisited. Nat. Med. 23, 1124-1134.

Beccari, L., Moris, N., Girgin, M., Turner, D.A., Baillie-Johnson, P., Cossy, A.-C., Lutolf, M.P., Duboule, D., Arias, A.M., 2018. Multi-axial self-organization properties of mouse embryonic stem cells into gastruloids. Nature 562, 272-276.

Ben-Haim, T., Raviv, T.R., 2022. Graph neural network for cell tracking in microscopy videos, in: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T., editors. Lecture Notes in Computer Science. Springer Nature Switzerland, pp. 610–626.

Bento, M., Fantini, I., Park, J., Rittner, L., Frayne, R., 2022. Deep learning in large and multi-site structural brain MR imaging datasets. Front. Neuroinform. 15, 805669.

Bertolo, A., Gemperli, A., Gruber, M., Gantenbein, B., Baur, M., Potzel, T., Stoyanov, J., 2015. In vitro cell motility as a potential mesenchymal stem cell marker for multipotency. Stem Cells Transl. Med. 4, 84-90.

Birk, S., Bonafonte-Pardas, I., Feriz, A.M., Boxall, A., Agirre, E., Memi, F., Maguza, A., Yadav, A., Armingol, E., Fan, R., et al., 2025. Quantitative characterization of cell niches in spatially resolved omics data. Nat. Genet. 57, 897-909.

Bricken, T., Templeton, A., Batson, J., Chen, B., Jermyn, A., Conerly, T., Turner, N., Anil, C., Denison, C., Askell, A., et al., 2023. Towards monosemanticity: decomposing language models with dictionary learning. Transformer Circuits Thread. https://transformer-circuits.pub/2023/monosemantic-features/index.html (accessed 20 December 2025).

Brixi, G., Durrant, M.G., Ku, J., Poli, M., Brockman, G., Chang, D., Gonzalez, G.A., King, S.H., Li, D.B., Merchant, A.T., et al., 2025. Genome modeling and design across all domains of life with Evo 2. bioRxiv. https://doi.org/10.1101/2025.02.18.638918.

Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al., 2020. Language models are few-shot learners, in: Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20. Curran Associates Inc., Red Hook, NY, USA, pp. 1877–1901.

Buggenthin, F., Buettner, F., Hoppe, P.S., Endele, M., Kroiss, M., Strasser, M., Schwarzfischer, M., Loeffler, D., Kokkaliaris, K.D., Hilsenbeck, O., et al., 2017. Prospective identification of hematopoietic lineage choice by deep learning. Nat. Methods 14, 403-406.

Bunne, C., Stark, S.G., Gut, G., del Castillo, J.S., Levesque, M., Lehmann, K.-V., Pelkmans, L., Krause, A., Ratsch, G., 2023. Learning single-cell perturbation responses using neural optimal transport. Nat. Methods 20, 1759-1768.

Cai, L., Wu, Y., Gao, J., 2019. DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network. BMC Bioinformatics 20, 665.

Carrillo-Perez, F., Pizurica, M., Zheng, Y., Nandi, T.N., Madduri, R., Shen, J., Gevaert, O., 2025. Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models. Nat. Biomed. Eng. 9, 320-332.

Cerneckis, J., Cai, H., Shi, Y., 2024. Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications. Signal Transduction and Targeted Therapy 9, 112.

Chang, Y.-H., Abe, K., Yokota, H., Sudo, K., Nakamura, Y., Lin, C.-Y., Tsai, M.-D., 2017. Human induced pluripotent stem cell region recognition in microscopy images using Convolutional Neural Networks, in: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Presented at the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4058–4061.

Chen, G., Liu, Z.-P., 2022. Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data. Bioinformatics 38, 4522-4529.

Chen, H., Venkatesh, M.S., Ortega, J.G., Mahesh, S.V., Nandi, T.N., Madduri, R.K., Pelka, K., Theodoris, C.V., 2024. Quantized multi-task learning for context-specific representations of gene network dynamics. bioRxiv. https://doi.org/10.1101/2024.08.16.608180.

Chen, J., Ding, L., Viana, M.P., Hendershott, M.C., Yang, R., Mueller, I., Rafelski, S., 2018. The Allen Cell Structure Segmenter: a new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy images.

Chen, J., Xu, L., Li, X., Park, S., 2023. Deep learning models for cancer stem cell detection: a brief review. Front. Immunol. 14, 1214425.

Chen, K.H., Boettiger, A.N., Moffitt, J.R., Wang, S., Zhuang, X., 2015. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090.

Chen, S., Luo, Y., Gao, H., Li, F., Chen, Y., Li, J., You, R., Hao, M., Bian, H., Xi, X., et al., 2022a. hECA: the cell-centric assembly of a cell atlas. iScience 25, 104318.

Chen, W., Guillaume-Gentil, O., Rainer, P.Y., Gabelein, C.G., Saelens, W., Gardeux, V., Klaeger, A., Dainese, R., Zachara, M., Zambelli, T., et al., 2022b. Live-seq enables temporal transcriptomic recording of single cells. Nature 608, 733-740.

Chilinski, M., Plewczynski, D., 2024. HiCDiffusion - diffusion-enhanced, transformer-based prediction of chromatin interactions from DNA sequences. BMC Genomics 25, 964.

Chu, S.-L., Sudo, K., Yokota, H., Abe, K., Nakamura, Y., Tsai, M.-D., 2023. Human induced pluripotent stem cell formation and morphology prediction during reprogramming with time-lapse bright-field microscopy images using deep learning methods. Comput. Methods Programs Biomed. 229, 107264.

Corso, G., Stark, H., Jegelka, S., Jaakkola, T., Barzilay, R., 2024. Graph neural networks. Nat. Rev. Methods Primers 4, 17.

Cui, W., Long, Q., Liu, W., Fang, C., Wang, X., Wang, P., Zhou, Y., 2025. Hierarchical graph transformer with contrastive learning for gene regulatory network inference. IEEE J. Biomed. Health Inform. 29, 690-699.

Damdimopoulou, P., Rodin, S., Stenfelt, S., Antonsson, L., Tryggvason, K., Hovatta, O., 2016. Human embryonic stem cells. Best Pract. Res. Cl. Ob. 31, 2-12.

Danielyan, L., Schwab, M., Siegel, G., Brawek, B., Garaschuk, O., Asavapanumas, N., Buadze, M., Lourhmati, A., Wendel, H.-P., Avci-Adali, M., et al., 2020. Cell motility and migration as determinants of stem cell efficacy. EBioMedicine 60, 102989.

de Morree, A., Rando, T.A., 2023. Regulation of adult stem cell quiescence and its functions in the maintenance of tissue integrity. Nat. Rev. Mol. Cell Biol. 24, 334-354.

DeepSeek-AI, Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., et al., 2025. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arXiv. https://doi.org/10.48550/arXiv.2501.12948.

Devlin, J., Chang, M.-W., Lee, K., Toutanova, K., 2019. BERT: pre-training of deep bidirectional transformers for language understanding, in: Burstein, J., Doran, C., Solorio, T., editors. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Presented at the NAACL-HLT 2019, Association for Computational Linguistics, Minneapolis, Minnesota, pp. 4171–4186.

Ding, T., Wagner, S.J., Song, A.H., Chen, R.J., Lu, M.Y., Zhang, A., Vaidya, A.J., Jaume, G., Shaban, M., Kim, A., et al., 2025. A multimodal whole-slide foundation model for pathology. Nat. Med. 31, 3749-3761.

Dirk, R., Fischer, J.L., Schardt, S., Ankenbrand, M.J., Fischer, S.C., 2023. Recognition and reconstruction of cell differentiation patterns with deep learning. PLoS Comput. Biol. 19, e1011582.

Donovan-Maiye, R.M., Brown, J.M., Chan, C.K., Ding, L., Yan, C., Gaudreault, N., Theriot, J.A., Maleckar, M.M., Knijnenburg, T.A., Johnson, G.R., 2022. A deep generative model of 3D single-cell organization. PLoS Comput. Biol. 18, e1009155.

Ergen, C., Pour Amiri, V.V., Kim, M., Kronfeld, O., Streets, A., Gayoso, A., Yosef, N., 2025. Scvi-hub: an actionable repository for model-driven single-cell analysis. Nat. Methods 22, 1836-1845.

Fan, J., Zeng, Y., 2023. Challenging deep learning models with image distortion based on the abutting grating illusion. Patterns (N Y) 4, 100695.

Fan, Y., Ma, X., 2021. Gene regulatory network inference using 3D convolutional neural network. Proc. Conf. AAAI Artif. Intell. 35, 99-106.

Finn, R.D., Bateman, A., Clements, J., Coggill, P., Eberhardt, R.Y., Eddy, S.R., Heger, A., Hetherington, K., Holm, L., Mistry, J., et al., 2014. Pfam: the protein families database. Nucleic Acids Res. 42, D222-D230.

Fudenberg, G., Kelley, D.R., Pollard, K.S., 2020. Predicting 3D genome folding from DNA sequence with Akita. Nat. Methods 17, 1111-1117.

Gan, Y., Hu, X., Zou, G., Yan, C., Xu, G., 2022. Inferring gene regulatory networks from single-cell transcriptomic data using bidirectional RNN. Front. Oncol. 12, 899825.

Gao, V.R., Yang, R., Das, A., Luo, R., Luo, H., McNally, D.R., Karagiannidis, I., Rivas, M.A., Wang, Z.-M., Barisic, D., et al., 2024. ChromaFold predicts the 3D contact map from single-cell chromatin accessibility. Nat. Commun. 15, 9432.

Gibson, D., Tran, T., Raveendran, V., Bonnet, C., Siu, N., Vinet, M., Stoddard-Bennett, T., Arnold, C., Deng, S.X., Speier, W., 2023. Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency. Front. Med. (Lausanne) 10, 1270570.

Greenwald, N.F., Miller, G., Moen, E., Kong, A., Kagel, A., Dougherty, T., Fullaway, C.C., McIntosh, B.J., Leow, K.X., Schwartz, M.S., et al., 2022. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555-565.

Gregor, B.W., Coston, M.E., Adams, E.M., Arakaki, J., Borensztejn, A., Do, T.P., Fuqua, M.A., Haupt, A., Hendershott, M.C., Leung, W., et al., 2024. Automated human induced pluripotent stem cell culture and sample preparation for 3D live-cell microscopy. Nat. Protoc. 19, 565-594.

Guan, B.X., Bhanu, B., Theagarajan, R., Liu, H., Talbot, P., Weng, N., 2021. Human embryonic stem cell classification: random network with autoencoded feature extractor. J. Biomed. Opt. 26, 052913.

Hanai, Y., Ishihata, H., Zhang, Z., Maruyama, R., Kasai, T., Kameda, H., Sugiyama, T., 2022. Temporal and locational values of images affecting the deep learning of cancer stem cell morphology. Biomedicines 10, 941.

Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., Song, L., 2024. Large-scale foundation model on single-cell transcriptomics. Nat. Methods 21, 1481-1491.

Harrison, S.E., Sozen, B., Christodoulou, N., Kyprianou, C., Zernicka-Goetz, M., 2017. Assembly of embryonic and extraembryonic stem cells to mimic embryogenesis in vitro. Science 356, eaal1810.

Hawkins-Hooker, A., Visona, G., Narendra, T., Rojas-Carulla, M., Scholkopf, B., Schweikert, G., 2023. Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning. Nat. Commun. 14, 4750.

He, K., Zhang, X., Ren, S., Sun, J., 2015. Deep Residual Learning for Image Recognition. arXiv. https://doi.org/10.48550/arXiv.1512.03385.

He, L., Li, M., Wang, X., Wu, X., Yue, G., Wang, T., Zhou, Y., Lei, B., Zhou, G., 2024. Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures. BMC Biol. 22, 1.

He, S., Zhu, Y., Tavakol, D.N., Ye, H., Lao, Y.-H., Zhu, Z., Xu, C., Chauhan, S., Garty, G., Tomer, R., et al., 2025. Squidiff: predicting cellular development and responses to perturbations using a diffusion model. Nat. Methods 23, 65-77.

Hegde, A., Nguyen, T., Cheng, J., 2025. Machine Learning Methods for Gene Regulatory Network Inference. arXiv. https://doi.org/10.48550/arXiv.2504.12610.

Heydari, T., Heidari, M., Mashinchian, O., Wojcik, M., Xu, K., Dalby, M.J., Mahmoudi, M., Ejtehadi, M.R., 2017. Development of a virtual cell model to predict cell response to substrate topography. ACS Nano 11, 9084-9092.

Hirose, T., Kotoku, J., Toki, F., Nishimura, E.K., Nanba, D., 2021. Label-free quality control and identification of human keratinocyte stem cells by deep learning-based automated cell tracking. Stem Cells 39, 1091-1100.

Horst, F., Rempe, M., Heine, L., Seibold, C., Keyl, J., Baldini, G., Ugurel, S., Siveke, J., Grunwald, B., Egger, J., et al., 2024. CellViT: vision transformers for precise cell segmentation and classification. Med. Image Anal. 94, 103143.

Hu, X., Chu, L., Pei, J., Liu, W., Bian, J., 2021. Model complexity of deep learning: a survey. Knowl. Inf. Syst. 63, 2585-2619.

Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017. Densely connected convolutional networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Presented at the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269.

Huben, R., Cunningham, H., Smith, L., Ewart, A., Sharkey, L., 2024. Sparse autoencoders find highly interpretable features in language models, in: Kim, B., Yue, Y., Chaudhuri, S., Fragkiadaki, K., Khan, M., Sun, Y., editors. International Conference on Learning Representations. pp. 7827–7845.

Ikeda, M., Doi, D., Ebise, H., Ozaki, Y., Fujii, M., Kikuchi, T., Yoshida, K., Takahashi, J., 2024. Validation of non-destructive morphology-based selection of cerebral cortical organoids by paired morphological and single-cell RNA-seq analyses. Stem Cell Rep. 19, 1635-1646.

Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H., 2021. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 203-211.

Joy, D.A., Libby, A.R.G., McDevitt, T.C., 2021. Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis. Stem Cell Rep. 16, 1317-1330.

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Zidek, A., Potapenko, A., et al., 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583-589.

Kalai, A.T., Nachum, O., Vempala, S.S., Zhang, E., 2025. Why Language Models Hallucinate. arXiv. https://doi.org/10.48550/arXiv.2509.04664.

Kalfon, J., Samaran, J., Peyre, G., Cantini, L., 2025. scPRINT: pre-training on 50 million cells allows robust gene network predictions. Nat. Commun. 16, 3607.

Kamimoto, K., Stringa, B., Hoffmann, C.M., Jindal, K., Solnica-Krezel, L., Morris, S.A., 2023. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742-751.

Kanda, G.N., Tsuzuki, T., Terada, M., Sakai, N., Motozawa, N., Masuda, T., Nishida, M., Watanabe, C.T., Higashi, T., Horiguchi, S.A., et al., 2022. Robotic search for optimal cell culture in regenerative medicine. eLife 11, e77007.

Kang, M., Gulati, G.S., Brown, E.L., Qi, Z., Avagyan, S., Armenteros, J.J.A., Gleyzer, R., Zhang, W., Steen, C.B., D’Silva, J.P., et al., 2025. Improved reconstruction of single-cell developmental potential with CytoTRACE 2. Nat. Methods 22, 2258-2263.

Kang, M., Lee, S., Lee, D., Kim, S., 2020. Learning cell-type-specific gene regulation mechanisms by multi-attention based deep learning with regulatory latent space. Front. Genet. 11, 869.

Karagiannis, P., Takahashi, K., Saito, M., Yoshida, Y., Okita, K., Watanabe, A., Inoue, H., Yamashita, J.K., Todani, M., Nakagawa, M., et al., 2019. Induced pluripotent stem cells and their use in human models of disease and development. Physiol. Rev. 99, 79-114.

Kartha, V.K., Duarte, F.M., Hu, Y., Ma, S., Chew, J.G., Lareau, C.A., Earl, A., Burkett, Z.D., Kohlway, A.S., Lebofsky, R., et al., 2022. Functional inference of gene regulation using single-cell multi-omics. Cell Genom. 2, 100166.

Kelley, D.R., Snoek, J., Rinn, J.L., 2016. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 26, 990-999.

Kim, D., Tran, A., Kim, H.J., Lin, Y., Yang, J.Y.H., Yang, P., 2023. Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data. npj Syst. Biol. Appl. 9, 51.

Kim, H., Park, K., Yon, J.-M., Kim, S.W., Lee, S.Y., Jeong, I., Jang, J., Lee, S., Cho, D.-W., 2022. Predicting multipotency of human adult stem cells derived from various donors through deep learning. Sci. Rep. 12, 21614.

Kircher, M.F., Gambhir, S.S., Grimm, J., 2011. Noninvasive cell-tracking methods. Nat. Rev. Clin. Oncol. 8, 677-688.

Kolides, A., Nawaz, A., Rathor, A., Beeman, D., Hashmi, M., Fatima, S., Berdik, D., Al-Ayyoub, M., Jararweh, Y., 2023. Artificial intelligence foundation and pre-trained models: Fundamentals, applications, opportunities, and social impacts. Simul. Model. Pract. Th. 126, 102754.

Kulmanov, M., Guzman-Vega, F.J., Duek Roggli, P., Lane, L., Arold, S.T., Hoehndorf, R., 2024. Protein function prediction as approximate semantic entailment. Nat. Mach. Intell. 6, 220-228.

Kusumoto, D., Lachmann, M., Kunihiro, T., Yuasa, S., Kishino, Y., Kimura, M., Katsuki, T., Itoh, S., Seki, T., Fukuda, K., 2018. Automated deep learning-based system to identify endothelial cells derived from induced pluripotent stem cells. Stem Cell Rep. 10, 1687-1695.

Kusumoto, D., Seki, T., Sawada, H., Kunitomi, A., Katsuki, T., Kimura, M., Ito, S., Komuro, J., Hashimoto, H., Fukuda, K., et al., 2021. Anti-senescent drug screening by deep learning-based morphology senescence scoring. Nat. Commun. 12, 257.

Kusumoto, D., Yuasa, S., 2019. The application of convolutional neural network to stem cell biology. Inflamm. Regen. 39, 14.

Lal, A., Chiang, Z.D., Yakovenko, N., Duarte, F.M., Israeli, J., Buenrostro, J.D., 2021. Deep learning-based enhancement of epigenomics data with AtacWorks. Nat. Commun. 12, 1507.

Larijani, B., Esfahani, E.N., Amini, P., Nikbin, B., Alimoghaddam, K., Amiri, S., Malekzadeh, R., Yazdi, N.M., Ghodsi, M., Dowlati, Y., et al., 2012. Stem Cell Therapy in Treatment of Different Diseases. Acta Med. Iran. 50, 79-96.

LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521, 436-444.

Lecun, Y., Bottou, L., Bengio, Y., Haffner, P., 1998. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278-2324.

Li, G., Yang, Y., Van Buren, E., Li, Y., 2019. Dropout imputation and batch effect correction for single-cell RNA sequencing data. J. Bio-X Res. 02, 169-177.

Li, J., Guan, Z., Wang, J., Cheung, C.Y., Zheng, Y., Lim, L.-L., Lim, C.C., Ruamviboonsuk, P., Raman, R., Corsino, L., et al., 2024. Integrated image-based deep learning and language models for primary diabetes care. Nat. Med. 30, 2886-2896.

Li, M., Izpisua Belmonte, J.C., 2018. Deconstructing the pluripotency gene regulatory network. Nat. Cell Biol. 20, 382-392.

Li, Q., Han, Z., Wu, X.-M., 2018. Deeper insights into graph convolutional networks for semi-supervised learning. Presented at the Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press, New Orleans, Louisiana, USA, pp. 3538–3545.

Li, X., Zhang, Y., Wu, J., Dai, Q., 2023. Challenges and opportunities in bioimage analysis. Nat. Methods 20, 958-961.

Li, Y., Zeng, M., Zhu, J., Liu, L., Wang, F., Huang, L., Yang, F., Li, M., Yao, J., 2025. Genetic-to-chemical perturbation transfer learning through unified multimodal molecular representations. bioRxiv. https://doi.org/10.1101/2025.02.02.635055.

Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J., 2022. A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33, 6999-7019.

Lien, C.-Y., Chen, T.-T., Tsai, E.-T., Hsiao, Y.-J., Lee, N., Gao, C.-E., Yang, Y.-P., Chen, S.-J., Yarmishyn, A.A., Hwang, D.-K., et al., 2023. Recognizing the differentiation degree of human induced pluripotent stem cell-derived retinal pigment epithelium cells using machine learning and deep learning-based approaches. Cells 12, 211.

Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al., 2023. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123-1130.

Liu, B., Greenwood, N.F., Bonzanini, J.E., Motmaen, A., Meyerberg, J., Dao, T., Xiang, X., Ault, R., Sharp, J., Wang, C., et al., 2025. Design of high-specificity binders for peptide-MHC-I complexes. Science 389, 386-391.

Liu, J., Castillo-Hair, S.M., Du, L.Y., Wang, Y., Carte, A.N., Colomer-Rosell, M., Yin, C., Seelig, G., Schier, A.F., 2024a. Dissecting the regulatory logic of specification and differentiation during vertebrate embryogenesis. bioRxiv. https://doi.org/10.1101/2024.08.27.609971.

Liu, X., Yan, J., Kirschner, M.W., 2024b. Cell size homeostasis is tightly controlled throughout the cell cycle. PLoS Biol. 22, e3002453.

Liu, Y.Y.F., Lu, Y., Oh, S., Conduit, G.J., 2020. Machine learning to predict mesenchymal stem cell efficacy for cartilage repair. PLoS Comput. Biol. 16, e1008275.

Lopez, R., Regier, J., Cole, M.B., Jordan, M.I., Yosef, N., 2018. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053-1058.

Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-Lopez, C., Misharin, A.V., Theis, F.J., 2023. Biologically informed deep learning to query gene programs in single-cell atlases. Nat. Cell Biol. 25, 337-350.

Loukas, A., 2020. What graph neural networks cannot learn: depth vs width. arXiv. https://doi.org/10.48550/arXiv.1907.03199.

Lu, X., Zhao, T., 2013. Clinical therapy using iPSCs: hopes and challenges. Genom. Proteinom. Bioinf. 11, 294-298.

Lubeck, E., Coskun, A.F., Zhiyentayev, T., Ahmad, M., Cai, L., 2014. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360-361.

Luo, Z., Ma, Q., Sun, S., Li, N., Wang, H., Ying, Z., Ke, S., 2023. Exon-intron boundary inhibits m6A deposition, enabling m6A distribution hallmark, longer mRNA half-life and flexible protein coding. Nat. Commun. 14, 4172.

Luo, Z., Zhang, J., Fei, J., Ke, S., 2022. Deep learning modeling m6A deposition reveals the importance of downstream cis-element sequences. Nat. Commun. 13, 2720.

Luong, T.Q., Zhang, X., Jie, Z., Sun, P., Jin, X., Li, H., 2024. ReFT: Reasoning with Reinforced Fine-Tuning. arXiv. https://doi.org/10.48550/arXiv.2401.08967.

Lynch, A.W., Brown, M., Meyer, C.A., 2023. Multi-batch single-cell comparative atlas construction by deep learning disentanglement. Nat. Commun. 14, 4126.

Lynch, A.W., Theodoris, C.V., Long, H.W., Brown, M., Liu, X.S., Meyer, C.A., 2022. MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. Nat. Methods 19, 1097-1108.

Ma, J., Xie, R., Ayyadhury, S., Ge, C., Gupta, A., Gupta, R., Gu, S., Zhang, Y., Lee, G., Kim, J., et al., 2024. The multi-modality cell segmentation challenge: towards universal solutions. Nat. Methods 21, 1103-1113.

Ma, W., Fu, Y., Bao, Y., Wang, Z., Lei, B., Zheng, W., Wang, C., Liu, Y., 2023. DeepSATA: a deep learning-based sequence analyzer incorporating the transcription factor binding affinity to dissect the effects of non-coding genetic variants. Int. J. Mol. Sci. 24, 12023.

Mahla, R.S., 2016. Stem cells applications in regenerative medicine and disease therapeutics. Int. J. Cell Biol. 2016, 6940283.

Mamaeva, A., Krasnova, O., Khvorova, I., Kozlov, K., Gursky, V., ra-ra, kwa-kwa, Tikhonova, O., Neganova, I., 2022. Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks. Int. J. Mol. Sci. 24, 140.

Mao, G., Pang, Z., Zuo, K., Wang, Q., Pei, X., Chen, X., Liu, J., 2023. Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks. Brief. Bioinform. 24, bbad414.

Maska, M., Ulman, V., Delgado-Rodriguez, P., Gomez-de-Mariscal, E., Necasova, T., Guerrero Pena, F.A., Ren, T.I., Meyerowitz, E.M., Scherr, T., Loffler, K., et al., 2023. The cell tracking challenge: 10 years of objective benchmarking. Nat. Methods 20, 1010-1020.

Maska, M., Ulman, V., Svoboda, D., Matula, P., Matula, P., Ederra, C., Urbiola, A., Espana, T., Venkatesan, S., Balak, D.M.W., et al., 2014. A benchmark for comparison of cell tracking algorithms. Bioinformatics 30, 1609-1617.

Minnoye, L., Taskiran, I.I., Mauduit, D., Fazio, M., Van Aerschot, L., Hulselmans, G., Christiaens, V., Makhzami, S., Seltenhammer, M., Karras, P., et al., 2020. Cross-species analysis of enhancer logic using deep learning. Genome Res. 30, 1815-1834.

Mirza, M., Osindero, S., 2014. Conditional Generative Adversarial Nets. ArXiv abs/1411.1784.

Mistry, J., Chuguransky, S., Williams, L., Qureshi, M., Salazar, G.A., Sonnhammer, E.L.L., Tosatto, S.C.E., Paladin, L., Raj, S., Richardson, L.J., et al., 2020. Pfam: the protein families database in 2021. Nucleic Acids Res. 49, D412-D419.

Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., Van Valen, D., 2019. Deep learning for cellular image analysis. Nat. Methods 16, 1233-1246.

Mohammad, S., Roy, A., Karatzas, A., Sarver, S.L., Anagnostopoulos, I., Chowdhury, F., 2024. Deep learning powered identification of differentiated early mesoderm cells from pluripotent stem cells. Cells 13, 534.

Morrow, A.K., Hughes, J.W., Singh, J., Joseph, A.D., Yosef, N., 2021. Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning. Nucleic Acids Res. 49, e110.

Moshkov, N., Bornholdt, M., Benoit, S., Smith, M., McQuin, C., Goodman, A., Senft, R.A., Han, Y., Babadi, M., Horvath, P., et al., 2024. Learning representations for image-based profiling of perturbations. Nat. Commun. 15, 1594.

Muncie-Vasic, J.M., Sinha, T., Clark, A.P., Brower, E.F., Saucerman, J.J., Black, B.L., Bruneau, B.G., 2025. MEF2C controls segment-specific gene regulatory networks that direct heart tube morphogenesis. Genes Dev. 39, 1451-1467.

Ng-Kee-Kwong, J., Philps, B., Smith, F.N.C., Sobieska, A., Chen, N., Alabert, C., Bilen, H., Buonomo, S.C.B., 2024. Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics. bioRxiv. https://doi.org/10.1101/2024.05.09.593366.

Norman, T.M., Horlbeck, M.A., Replogle, J.M., Ge, A.Y., Xu, A., Jost, M., Gilbert, L.A., Weissman, J.S., 2019. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365, 786-793.

OpenAI, 2025. OpenAI o1 System Card. https://openai.com/index/openai-o1-system-card/ (accessed 19 January 2025).

Palma, A., Theis, F.J., Lotfollahi, M., 2025. Predicting cell morphological responses to perturbations using generative modeling. Nat. Commun. 16, 505.

Pan, Y., Xia, Y., Zhou, T., Fulham, M., 2017. Cell image segmentation using bacterial foraging optimization. Appl. Soft Comput. 58, 770-782.

Park, K., Lee, J.Y., Lee, S.Y., Jeong, I., Park, S.-Y., Kim, J.W., Nam, S.A., Kim, H.W., Kim, Y.K., Lee, S., 2023. Deep learning predicts the differentiation of kidney organoids derived from human induced pluripotent stem cells. Kidney Res. Clin. Pract. 42, 75-85.

Pizarro, R., Assemlal, H.-E., De Nigris, D., Elliott, C., Antel, S., Arnold, D., Shmuel, A., 2019. Using deep learning algorithms to automatically identify the brain MRI contrast: implications for managing large databases. Neuroinform. 17, 115-130.

Powell, K.A., Bohrer, L.R., Stone, N.E., Hittle, B., Anfinson, K.R., Luangphakdy, V., Muschler, G., Mullins, R.F., Stone, E.M., Tucker, B.A., 2023. Automated human induced pluripotent stem cell colony segmentation for use in cell culture automation applications. SLAS Technol. 28, 416-422.

Prangemeier, T., Reich, C., Koeppl, H., 2020. Attention-based transformers for instance segmentation of cells in microstructures, in: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 700–707.

Pratapa, A., Jalihal, A.P., Law, J.N., Bharadwaj, A., Murali, T.M., 2020. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17, 147-154.

Prosz, A., Pipek, O., Borcsok, J., Palla, G., Szallasi, Z., Spisak, S., Csabai, I., 2024. Biologically informed deep learning for explainable epigenetic clocks. Sci. Rep. 14, 1306.

Qian, L., Dong, Z., Guo, T., 2025. Grow AI virtual cells: three data pillars and closed-loop learning. Cell Res. 35, 319-321.

Qiu, M., Yang, G., 2013. Drift correction for fluorescence live cell imaging through correlated motion identification, in: 2013 IEEE 10th International Symposium on Biomedical Imaging. Presented at the 2013 IEEE 10th International Symposium on Biomedical Imaging, pp. 452–455.

Radford, A., Narasimhan, K., 2018. Improving language understanding by generative pre-training.

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., 2019. Language models are unsupervised multitask learners.

Ravi, M., Paramesh, V., Kaviya, S. r., Anuradha, E., Solomon, F.D.P., 2015. 3D cell culture systems: advantages and applications. J. Cell. Physiol. 230, 16-26.

Ren, E., Kim, S., Mohamad, S., Huguet, S., Shi, Y., Cohen, A., Piddini, E., Salas, R.C., 2021. Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSC. bioRxiv. https://doi.org/10.1101/2021.07.31.454574.

Rezania, A., Bruin, J.E., Xu, J., Narayan, K., Fox, J.K., O’Neil, J.J., Kieffer, T.J., 2013. Enrichment of human embryonic stem cell-derived NKX6.1-expressing pancreatic progenitor cells accelerates the maturation of insulin-secreting cells in vivo. Stem Cells 31, 2432-2442.

Ronneberger, O., Fischer, P., Brox, T., 2015. U-Net: convolutional networks for biomedical image segmentation. arXiv. https://doi.org/10.48550/arXiv.1505.04597.

Roohani, Y., Huang, K., Leskovec, J., 2024. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat. Biotechnol. 42, 927-935.

Roohani, Y.H., Hua, T.J., Tung, P.-Y., Bounds, L.R., Yu, F.B., Dobin, A., Teyssier, N., Adduri, A., Woodrow, A., Plosky, B.S., et al., 2025. Virtual cell challenge: toward a turing test for the virtual cell. Cell 188, 3370-3374.

Sadria, M., Bury, T.M., 2024. FateNet: an integration of dynamical systems and deep learning for cell fate prediction. Bioinformatics 40, btae525.

Sarker, I.H., 2021. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2, 420.

Schaefer, M., Peneder, P., Malzl, D., Lombardo, S.D., Peycheva, M., Burton, J., Hakobyan, A., Sharma, V., Krausgruber, T., Sin, C., et al., 2025. Multimodal learning enables chat-based exploration of single-cell data. Nat. Biotechnol. 1-11.

Scherr, T., Loffler, K., Neumann, O., Mikut, R., 2021. On improving an already competitive segmentation algorithm for the cell tracking challenge - lessons learned. bioRxiv. https://doi.org/10.1101/2021.06.26.450019.

Schneider, M.V., Orchard, S., 2011. Omics technologies, data and bioinformatics principles, in: Mayer, B., editor. Bioinformatics for Omics Data. Humana Press, pp. 3–30.

Schwessinger, R., Gosden, M., Downes, D., Brown, R.C., Oudelaar, A.M., Telenius, J., Teh, Y.W., Lunter, G., Hughes, J.R., 2020. DeepC: predicting 3D genome folding using megabase-scale transfer learning. Nat. Methods 17, 1118-1124.

Seninge, L., Anastopoulos, I., Ding, H., Stuart, J., 2021. VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics. Nat. Commun. 12, 5684.

Shah, S., Lubeck, E., Zhou, W., Cai, L., 2016. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342-357.

Shen, D., Wu, G., Suk, H.-I., 2017. Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221-248.

Shen, J., Chen, N., Niu, B., Zhong, J., Liu, R., 2025. FatePredictor: cell fate decision-making prediction with an ensemble deep learning model. Innovation 6.

Shu, H., Ding, F., Zhou, J., Xue, Y., Zhao, D., Zeng, J., Ma, J., 2022. Boosting single-cell gene regulatory network reconstruction via bulk-cell transcriptomic data. Briefings Bioinf. 23, bbac389.

Simonyan, K., Zisserman, A., 2015. Very deep convolutional networks for large-scale image recognition. arXiv. https://doi.org/10.48550/arXiv.1409.1556.

Song, Y., Fan, J., Huang, H., Chen, M., Cai, W., 2025. Cell as point: one-stage framework for efficient cell tracking. arXiv. https://doi.org/10.48550/arXiv.2411.14833.

Su, Y.-T., Lu, Y., Chen, M., Liu, A.-A., 2017. Spatiotemporal joint mitosis detection using CNN-LSTM network in time-lapse phase contrast microscopy images. IEEE Access 5, 18033-18041.

Suyama, T., Takemoto, Y., Miyauchi, H., Kato, Y., Matsuzaki, Y., Kato, R., 2022. Morphology-based noninvasive early prediction of serial-passage potency enhances the selection of clone-derived high-potency cell bank from mesenchymal stem cells. Inflammation Regener. 42, 30.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions, in: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1–9.

Tan, J., Shenker-Tauris, N., Rodriguez-Hernaez, J., Wang, E., Sakellaropoulos, T., Boccalatte, F., Thandapani, P., Skok, J., Aifantis, I., Fenyo, D., et al., 2023. Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening. Nat. Biotechnol. 41, 1140-1150.

Templeton, A., Conerly, T., Marcus, J., Lindsey, J., Bricken, T., Chen, B., Pearce, A., Citro, C., Ameisen, E., Jones, A., et al., 2024. Scaling monosemanticity: extracting interpretable features from Claude 3 Sonnet. Transformer Circuits Thread. https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html (accessed 20 December 2025).

Theagarajan, R., Bhanu, B., 2019. DeephESC 2.0: deep generative multi adversarial networks for improving the classification of hESC. PLOS ONE 14, e0212849.

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. Transfer learning enables predictions in network biology. Nature 618, 616-624.

Tian, Z., Yu, T., Liu, J., Wang, T., Higuchi, A., 2023. Chapter One - Introduction to stem cells, in: Higuchi, A., Zhou, Y., Chiou, S.-H., editors. Progress in Molecular Biology and Translational Science, Stem Cell in Medicine. Academic Press, pp. 3–32.

Tran, H.T.N., Ang, K.S., Chevrier, M., Zhang, X., Lee, N.Y.S., Goh, M., Chen, J., 2020. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12.

Tsimbouri, P., Gadegaard, N., Burgess, K., White, K., Reynolds, P., Herzyk, P., Oreffo, R., Dalby, M.J., 2014. Nanotopographical effects on mesenchymal stem cell morphology and phenotype. J. Cell. Biochem. 115, 380-390.

Ulman, V., Maska, M., Magnusson, K.E.G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., et al., 2017. An objective comparison of cell-tracking algorithms. Nat. Methods 14, 1141-1152.

Vandereyken, K., Sifrim, A., Thienpont, B., Voet, T., 2023. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494-515.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., 2023. Attention is all you need. arXiv. https://doi.org/10.48550/arXiv.1706.03762.

Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., 2018. Graph attention networks. arXiv. https://doi.org/10.48550/arXiv.1710.10903.

Wagers, A.J., 2012. The stem cell niche in regenerative medicine. Cell Stem Cell 10, 362-369.

Waisman, A., La Greca, A., Mobbs, A.M., Scarafia, M.A., Santin Velazque, N.L., Neiman, G., Moro, L.N., Luzzani, C., Sevlever, G.E., Guberman, A.S., et al., 2019. Deep learning neural networks highly predict very early onset of pluripotent stem cell differentiation. Stem Cell Rep. 12, 845-859.

Wang, C.-H., Lin, G.-C., Fu, R.-H., Huang, Y.-C., Chen, S.-Y., Lin, S.-Z., Harn, H.-J., Shyu, W.-C., Huang, Y., Jeng, L.-B., et al., 2024. Neural stem cells derived from α-synuclein-knockdown iPS cells alleviate Parkinson’s disease. Cell Death Discov. 10, 407.

Wang, G., Zhao, J., Lin, Y., Liu, T., Zhao, Y., Zhao, H., 2025a. scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links. Nat. Commun. 16, 4994.

Wang, J., Ma, A., Ma, Q., Xu, D., Joshi, T., 2020. Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks. Comput. Struct. Biotechnol. J. 18, 3335-3343.

Wang, P., Wen, X., Li, H., Lang, P., Li, S., Lei, Y., Shu, H., Gao, L., Zhao, D., Zeng, J., 2023a. Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON. Nat. Commun. 14, 8459.

Wang, Q., Zhu, H., Hu, Y., Chen, Y., Wang, Y., Li, G., Li, Y., Chen, J., Zhang, X., Zou, J., et al., 2025b. CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer. Genome Biol. 26, 178.

Wang, S., Han, J., Huang, J., Islam, K., Shi, Y., Zhou, Y., Kim, D., Zhou, J., Lian, Z., Liu, Y., et al., 2023b. Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors. Stem Cell Res. Ther. 15, 74.

Warmflash, A., Sorre, B., Etoc, F., Siggia, E.D., Brivanlou, A.H., 2014. A method to recapitulate early embryonic spatial patterning in human embryonic stem cells. Nat. Methods 11, 847-854.

Wei, L., Yan, W., Shah, W., Zhang, Z., Wang, M., Liu, B., Xue, Z., Cao, Y., Hou, X., Zhang, K., et al., 2024. Advancements and challenges in stem cell transplantation for regenerative medicine. Heliyon 10, e35836.

Wei, Z., Hua, K., Wei, L., Ma, S., Jiang, R., Zhang, X., Li, Y., Wong, W.H., Wang, X., 2023. NeuronMotif: deciphering cis-regulatory codes by layer-wise demixing of deep neural networks. Proc. Natl. Acad. Sci. 120, e2216698120.

Xi, X., Li, J., Jia, J., Meng, Q., Li, C., Wang, X., Wei, L., Zhang, X., 2025. A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions. Nat. Commun. 16, 1284.

Xia, C., Colognori, D., Jiang, X.S., Xu, K., Doudna, J.A., 2025. Single-molecule live-cell RNA imaging with CRISPR-Csm. Nat. Biotechnol. 43, 2023-2030.

Xiong, L., Xu, K., Tian, K., Shao, Y., Tang, L., Gao, G., Zhang, M., Jiang, T., Zhang, Q.C., 2019. SCALE method for single-cell ATAC-seq analysis via latent feature extraction. Nat. Commun. 10, 4576.

Xiong, Z., Liu, S., Huang, F., Wang, Z., Liu, X., Zhang, Z., Zhang, W., 2023. Multi-relational contrastive learning graph neural network for drug-drug interaction event prediction. Proceedings of the AAAI Conference on Artificial Intelligence 37, 5339-5347.

Xu, J., Zhang, A., Liu, F., Zhang, X., 2023. STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data. Bioinformatics 39, btad165.

Xu, Y., Fleming, S., Tegtmeyer, M., McCarroll, S.A., Babadi, M., 2025a. Explainable modeling of single-cell perturbation data using attention and sparse dictionary learning. Cell Systems 16, 101245.

Xu, Z., Jain, S., Kankanhalli, M., 2025b. Hallucination is Inevitable: An Innate Limitation of Large Language Models. arXiv. https://doi.org/10.48550/arXiv.2401.11817.

Yamada, R., Okada, D., Wang, J., Basak, T., Koyama, S., 2021. Interpretation of omics data analyses. J. Hum. Genet. 66, 93-102.

Yan, R., Fan, C., Yin, Z., Wang, T., Chen, X., 2021. Potential applications of deep learning in single-cell RNA sequencing analysis for cell therapy and regenerative medicine. Stem Cells 39, 511-521.

Yang, R., Das, A., Gao, V.R., Karbalayghareh, A., Noble, W.S., Bilmes, J.A., Leslie, C.S., 2023. Epiphany: predicting Hi-C contact maps from 1D epigenomic signals. Genome Biol. 24, 134.

Yang, X., Liu, G., Feng, G., Bu, D., Wang, P., Jiang, J., Chen, S., Yang, Q., Miao, H., Zhang, Y., et al., 2024. GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model. Cell Res. 34, 830-845.

Yang, Y., Cui, Y., Zeng, X., Zhang, Y., Loza, M., Park, S.-J., Nakai, K., 2025. STAIG: spatial transcriptomics analysis via image-aided graph contrastive learning for domain exploration and alignment-free integration. Nat. Commun. 16, 1067.

Yazdi, R., Khotanlou, H., 2024. A survey on automated cell tracking: challenges and solutions. Multimed. Tools Appl. 83, 81511-81547.

Ye, B., Hongting, G., Zhuang, W., Chen, C., Yi, S., Tang, X., Jiang, A., Zhong, Y., 2024. Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature. J. Cell. Mol. Med. 28, e18564.

Yeo, G.H.T., Saksena, S.D., Gifford, D.K., 2021. Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions. Nat. Commun. 12, 3222.

Yi, K., Li, H., Xu, C., Zhong, G., Ding, Z., Zhang, G., Guan, X., Zhong, M., Li, G., Jiang, N., et al., 2023. Morphological feature recognition of different differentiation stages of induced ADSCs based on deep learning. Comput. Biol. Med. 159, 106906.

Yin, Q., Wu, M., Liu, Q., Lv, H., Jiang, R., 2019. DeepHistone: a deep learning approach to predicting histone modifications. BMC Genomics 20, 193.

Yuan, Y., Bar-Joseph, Z., 2019. Deep learning for inferring gene relationships from single-cell expression data. Proc. Natl. Acad. Sci. U. S. A. 116, 27151-27158.

Yuan, Y., Wang, T., Sims, J., Le, K., Undey, C., Oruklu, E., 2024. Cytopathic effect detection and clonal selection using deep learning. Pharm. Res. 41, 1659-1669.

Yucel, A.D., Gladyshev, V.N., 2024. The long and winding road of reprogramming-induced rejuvenation. Nat. Commun. 15, 1941.

Zakrzewski, W., Dobrzynski, M., Szymonowicz, M., Rybak, Z., 2019. Stem cells: past, present, and future. Stem Cell Res. Ther. 10, 68.

Zargari, A., Lodewijk, G.A., Mashhadi, N., Cook, N., Neudorf, C.W., Araghbidikashani, K., Hays, R., Kozuki, S., Rubio, S., Hrabeta-Robinson, E., et al., 2023. DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy. Cell Rep. Methods 3, 100500.

Zeng, Z., Cheng, Q., Yin, Z., Wang, B., Li, S., Zhou, Y., Guo, Q., Huang, X., Qiu, X., 2024. Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective. arXiv. https://doi.org/10.48550/arXiv.2412.14135.

Zhang, H., Nguyen, D.H., Tsuda, K., 2023a. Differentiable optimization layers enhance GNN-based mitosis detection. Sci. Rep. 13, 14306.

Zhang, Y., Imoto, S., 2024. Genome analysis through image processing with deep learning models. J. Hum. Genet. 69, 519-525.

Zhang, Y., Wang, L., Feng, Z., Cheng, H., McGuire, T.F., Ding, Y., Cheng, T., Gao, Y., Xie, X.-Q., 2016. StemCellCKB: an integrated stem cell-specific chemogenomics KnowledgeBase for target identification and systems-pharmacology research. J. Chem. Inf. Model. 56, 1995-2004.

Zhang, Z., Feng, F., Qiu, Y., Liu, J., 2023b. A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome. Nucleic Acids Res. 51, 5931-5947.

Zhang, Z., Leong, K.W., Vliet, K.V., Barbastathis, G., Ravasio, A., 2021. Deep learning for label-free nuclei detection from implicit phase information of mesenchymal stem cells. Biomed. Opt. Express 12, 1683-1706.

Zhang, Z., Zhao, X., Bindra, M., Qiu, P., Zhang, X., 2024. scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data. Nat. Commun. 15, 912.

Zhao, J., Zhou, Y., Bao, H., Zhao, X., Wang, X., Zhao, C., Qin, W., Lu, X., Xu, G., 2025. MODA: a graph convolutional network-based multi-omics integration framework for unraveling hub molecules and disease mechanisms. Brief. Bioinform. 26, bbaf532.

Zhao, M., He, W., Tang, J., Zou, Q., Guo, F., 2022. A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data. Brief. Bioinform. 23, bbab568.

Zheng, X., Zhang, D., Xu, M., Zeng, W., Zhou, R., Zhang, Y., Tang, C., Chen, L., Chen, L., Lin, J., 2021. Short-read and long-read RNA sequencing of mouse hematopoietic stem cells at bulk and single-cell levels. Sci. Data 8, 309.

Zhou, J., 2022. Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale. Nat. Genet. 54, 725-734.

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M., 2020. Graph neural networks: a review of methods and applications. AI Open 1, 57-81.

Zhou, J., Troyanskaya, O.G., 2015. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931-934.

Zhu, J., Zhang, Z., Xiang, Y., Xie, B., Dong, X., Xie, L., Zhou, P., Yao, R., Wang, X., Li, Y., et al., 2025. Cell Decoder: decoding cell identity with multi-scale explainable deep learning. Genome Biol. 26, 359.

Zhu, Y., Huang, R., Wu, Z., Song, S., Cheng, L., Zhu, R., 2021. Deep learning-based predictive identification of neural stem cell differentiation. Nat. Commun. 12, 2614.

Zinati, Y., Takiddeen, A., Emad, A., 2024. GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks. Nat. Commun. 15, 4055.

Zong, Y., Yu, T., Wang, X., Wang, Y., Hu, Z., Li, Y., 2022. conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics. bioRxiv. https://doi.org/10.1101/2022.01.14.476408.

Relative Articles

Supplements(0)

Cited By

Proportional views