Machine learning for perturbational single-cell omics

Attention Is All You Need

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

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

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MizAR 60 for Mizar 50

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · 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) · doi:10.18653/v1/n19-1423 · Cited by 54,325

Gene Ontology: tool for the unification of biology

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ImageNet Large Scale Visual Recognition Challenge

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Untitled work

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Rethinking the Inception Architecture for Computer Vision

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Language Models are Few-Shot Learners

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

Predicting Unseen Gene Perturbation Response Using Graph Neural Networks with Biological PriorsPreprint

Dip, S. A., & Zhang, L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.23.713780 · Cited by 1

Massively multiplex multimodal chemical screens at single-cell resolutionPreprint

Chen, K. Y., Lopez, R., Eraslan, B., Hata, M., Takeshima, Y., Makino, K., Kibayashi, T., Ichiyama, K., Biton, A., Huetter, J.-C., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.23.727396 · Cited by 1

Finding the volume dial in stem cell manufacturing: Bioinspired and bioengineered approaches to scale up

Kinney, M. A. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100356

Tools and Data Towards Supporting Quantitative Systems Pharmacology of the Eye – An OverviewPreprint

Kuepfer, L., Fuellen, G., & Stahnke, T. (2022) · Preprints.org · doi:10.20944/preprints202203.0164.v2

Simple Causal Relationships in Gene Expression Discovered through Deep Learned Collective VariablesPreprint

Wang, C.-H., Vetsigian, K., Lin, C., Firth, F., Bradley, G., Granovsky, L., & England, J. L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.18.524617

Protocol to assess fatal embolism risks from human stem cells

Ma, F., Zhang, J., Jin, X., Han, P., Liu, Y., Zhang, T., Yan, K., & Kang, Y. J. (2023) · STAR Protocols · doi:10.1016/j.xpro.2023.102268

Modeling interpretable correspondence between cell state and perturbation response with CellCapPreprint

Xu, Y., Fleming, S., Tegtmeyer, M., McCarroll, S. A., & Babadi, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.14.585078

Unveiling gene perturbation effects through gene regulatory networks inference from single-cell transcriptomic data

Corridori, C., Romeike, M., Nicoletti, G., Buecker, C., Suweis, S. S., Azaele, S., & Martello, G. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014067

Stochastic Modeling of Biophysical Responses to PerturbationPreprint

Chari, T., Gorin, G., & Pachter, L. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.04.602131

Single-Cell Drug Perturbations Prediction Using Machine Learning

Prajapati, M., Baliarsingh, S. K., Dev, P. P., Nayak, S., & Biswal, M. R. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-64067-4_3

Modeling CAR Response at the Single-Cell Level Using Conditional Optimal TransportPreprint

Driessen, A., Born, J., Castellanos-Rueda, R., Reddy, S. T., & Rapsomaniki, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.11.622906

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

Causal models and prediction in cell line perturbation experiments

Long, J. P., Yang, Y., Shimizu, S., Pham, T., & Do, K.-A. (2025) · BMC Bioinformatics · doi:10.1186/s12859-024-06027-7

Genetic-to-Chemical Perturbation Transfer Learning Through Unified Multimodal Molecular RepresentationsPreprint

Li, Y., Zeng, M., Zhu, J., Liu, L., Wang, F., Huang, L.-K., Yang, F., Li, M., & Yao, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.02.635055

A review on single-cell perturbation data analysis

Fu, Z., Yang, M., & Hou, L. (2025) · Scientia Sinica Mathematica · doi:10.1360/ssm-2024-0315

Quantum Cognition Machine Learning for Forecasting Chromosomal InstabilityPreprint

Di, G., Kirakosyan, V., Abanov, A. G., Busemeyer, J. R., Candelori, L., Hartmann, N., Lam, E. T., Musaelian, K., Samson, R., Steinacker, H., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.30.656882

Hunting for Targets

Zanders, E. D. (2025) · Springer eBooks · doi:10.1007/978-3-031-93949-5_5

ScAPE: A lightweight multitask learning baseline method to predict transcriptomic responses to perturbationsPreprint

Romero-Tapiador, S., Rodríguez-Mier, P., Garrido‐Rodríguez, M., Tolosana, R., Morales, A., & Sáez-Rodríguez, J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.08.674873

Advances in single-cell perturbation modeling

Qi, X., Zhao, L., Chen, R., & Zhao, Y. C. (2025) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb-2025-0682

Single-cell characterization and machine learning-based classification reveal the transcriptional identity and antigen-experienced features of peripheral CD4⁺CD8⁺ double-positive T cellsPreprint

Shin, E., Yun, S. G., & Cho, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.01.697324

Artificial Intelligence-Enabled Single-Cell Resolution and Spatial Plant Multi-omics

Babu, H. W. S., Manoj, M., Pallavi, P., Girigoswami, A., Girigoswami, K., Gowtham, P., Robert, A. A., & Anand, A. V. (2026) · CABI biotechnology series. · doi:10.1079/9781800628649.0005

scCAVAE: Modeling Synergistic Interactions in Perturbation Responses via Attention and Hierarchical Supervised Contrastive Learning

Li, X., Zhan, B., Zhang, J., & Yan, S. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356521

Predicting Chemo-immunotherapy Response in Early-Stage Hormone Receptor–Positive Breast Cancer Using Multimodal Single-Cell Analysis: Model Development and Validation Study (Preprint)Preprint

Josan, A. S. (2026) · doi:10.2196/preprints.93768

In Silico Perturbation Identifies Transcription Factors as Protective Targets in HSPCs After Irradiation

Tao, Z., Zhang, Q., Chen, Y., Lv, S., Huang, Q., Tian, H., Liu, Q., Li, C., Wang, Y., Lu, H., et al. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27083522

Codebook-Optimized Variational Autoencoder for Predicting Cellular Responses to Drug Perturbations

Wang, H., Zhang, Y., & Wu, C. (2026) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3212/1/012007

Single-cell gene regulatory network reconstruction and key regulator identification using a dual-channel fusion graph convolutional networkPreprint

Tang, R., Liu, J., Zhang, P., & Liang, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.05.730394

V3Cell: A Vision-Guided Virtual 3D Cell Framework for Phenotypic Modeling and Perturbation PredictionPreprint

Lu, Y., Xun, D., Xu, C., Zhu, X., Zhang, Z. D., Chen, P., Yang, X., Yan, Z., Rao, J., Huili, H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.23.734130

A Neural-Network-Free Calibration Matches or Beats Deep Single-Cell Perturbation Response Models Across Four Datasets

Sun, B., Zheng, H., Chen, J., & Jin, J. (2026) · Genes · doi:10.3390/genes17070816

Stable-Shift: Predicting Transcriptional Responses of Unseen Gene Perturbations Using Graph Neural Networks with Biological Priors

Dip, S. A., & Zhang, L. (2026) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3807503.3820871

Language-Enhanced Representation Learning for Single-Cell Transcriptomics

Yang, J., Liu, Z., Shi, Y., Zhang, Q., Nai, C., Fang, J., Long, Y., Zhang, Y., & Wang, X. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3819031

Network toxicology integrated with single-cell RNA sequencing reveals ROS-related targets of paraquat in pulmonary fibrosis

Zhang, M., Xue, S., Xu, M., Liu, C., & Hong, G. (2026) · Journal of Hazardous Materials Advances · doi:10.1016/j.hazadv.2026.101476

Reliable single-cell perturbations explain and improve model performancePreprint

Wang, X., Kuipers, J., Hugi, F., Platt, R. J., & Beerenwinkel, N. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.11.744177 · Cited by 4

Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance

Dai, Y., Wu, L., Xue, H., Zhou, L., Yang, Y., Wang, X., Lai, Y., Tang, Y., Huang, H., Chen, S., et al. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1921962

scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes

Tang, H., Li, H., Zhao, Q., Zhou, X., Peng, L., Cai, Y., Lin, R., Li, Y., Yuan, Y., Feng, W., et al. (2026) · BMC Genomics · doi:10.1186/s12864-026-13270-0

Predicting Single‐Cell Perturbation Responses Across Biological Contexts With a Deep Generative Model Integrating Optimal Transport

Wang, J., Liu, Z., Zhang, Z., Cao, Y., Ren, J., Cheng, P., Tian, J., Xie, L., Lu, X., Du, Z., et al. (2026) · Advanced Science · doi:10.1002/advs.77461

A transcription factor regulatory atlas for activity inference and perturbation prediction

Sugimoto, H., Tsuyuzaki, K., Zou, Z., Oki, S., Ohta, T., & Kawakami, E. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag897

Artificial intelligence in drug discovery: Integrating data, models, and mechanisms for next-generation therapeutics

Korutla, R., Garg, T., Pol, S. S., Shivandi, V., Gnanam, S., Padari, K. S., & Amal, S. (2026) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2026.100183 · Cited by 1

A mini-review on perturbation modelling across single-cell omic modalities

Gavriilidis, G. I., Vasileiou, V., Orfanou, A., Ishaque, N., & Psomopoulos, F. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.058 · Cited by 54

MultiPert: An adversarial alignment and dual attention framework for single-cell multi-omics perturbation prediction

Zhao, M., Tang, X., Li, J., Liang, C., Tang, J., & Guo, F. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014054 · Cited by 3

DeMixPert: Decomposed Response Modeling with Gaussian Mixtures for OOD Single-Cell Perturbation PredictionPreprint

Liu, J., Cao, X., Li, Y., Liu, B., Liang, J., Zhang, T., Sheng, X., & Cai, H. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2608.23114

Modeling single-cell perturbations using deep learning

Lotfollahi, M., Theis, F., & München, T. U. (2021)

Deep-learning-based isolation of perturbation-induced variations in single-cell data

(2023) · Nature Methods · doi:10.1038/s41592-023-01956-2