Machine learning for perturbational single-cell omics

Ji, Yuge; Lotfollahi, Mohammad; Wolf, F. Alexander; Theis, Fabian J.

doi:10.1016/j.cels.2021.05.016

Cited by 137Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisBioinformatics and Genomic Networks

Machine learning for perturbational single-cell omics

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Ji, Yuge, Lotfollahi, Mohammad, Wolf, F. Alexander, Theis, Fabian J.·2021·Cell Systems

DOI 10.1016/j.cels.2021.05.016

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Abstract

Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.

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Attention Is All You Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
MizAR 60 for Mizar 50
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
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 48,293 · see citing works
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Untitled work
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759 · see citing works
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108 · see citing works
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015 · see citing works
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807 · see citing works
Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174 · see citing works
Untitled work
(2016) · Cited by 11,679 · see citing works
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update
Kuleshov, M. V., Jones, M. R., Rouillard, A. D., Fernandez, N. F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S. L., Jagodnik, K. M., Lachmann, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw377 · Cited by 10,799 · see citing works
DrugBank 5.0: a major update to the DrugBank database for 2018
Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1037 · Cited by 8,404 · see citing works
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool
Chen, E. Y., Tan, C. M., Kou, Y., Duan, Q., Wang, Z., Meirelles, G. V., Clark, N. R., & Ma’ayan, A. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-128 · Cited by 7,814 · 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
Prototypical Networks for Few-shot LearningPreprint
Snell, J., Swersky, K., & Zemel, R. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.05175 · Cited by 5,863 · see citing works
The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257 · see citing works
Docking and scoring in virtual screening for drug discovery: methods and applications
Kitchen, D. B., Decornez, H. Y., Furr, J. R., & Bajorath, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1549 · Cited by 4,245 · see citing works
RNA velocity of single cells
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118 · see citing works
Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells
Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J. A., Thompson, I. R., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1111 · Cited by 4,051 · see citing works
Tumour heterogeneity and resistance to cancer therapies
Dagogo-Jack, I., & Shaw, A. T. (2018) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.166 · Cited by 3,846 · see citing works
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515 · see citing works
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277 · see citing works
On the Dependency of Cellular Protein Levels on mRNA Abundance
Liu, Y., Beyer, A., & Aebersold, R. (2016) · Cell · doi:10.1016/j.cell.2016.03.014 · Cited by 3,221 · see citing works
Generalizing RNA velocity to transient cell states through dynamical modeling
Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Theis, F. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0591-3 · Cited by 2,951 · see citing works
Self-citation
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761 · see citing works
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628 · see citing works
Combination therapy in combating cancer
Mokhtari, R. B., Homayouni, T. S., Baluch, N., Morgatskaya, E., Kumar, S., Das, B., & Yeger, H. (2017) · Oncotarget · doi:10.18632/oncotarget.16723 · Cited by 2,543 · 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
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
Predicting effects of noncoding variants with deep learning–based sequence model
Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338 · see citing works
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works
The human cell atlas
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Self-citation
Human organoids: model systems for human biology and medicine
Kim, J., Koo, B.-K., & Knoblich, J. A. (2020) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-020-0259-3 · Cited by 2,187 · 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
Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly
Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2857768 · Cited by 1,678 · see citing works
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works
The Human Cell Atlas
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457 · see citing works
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genomePreprint
Ji, Y.-R., Zhou, Z., Liu, H., & Davuluri, R. (2020) · bioRxiv · doi:10.1101/2020.09.17.301879 · Cited by 1,426 · see citing works
Self-citation
Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · see citing works
High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response
Gao, H., Korn, J. M., Ferretti, S., Monahan, J. E., Wang, Y., Singh, M., Zhang, C., Schnell, C., Yang, G., Zhang, Y., et al. (2015) · Nature Medicine · doi:10.1038/nm.3954 · Cited by 1,393 · see citing works
Single-cell genome sequencing: current state of the science
Gawad, C., Koh, W., & Quake, S. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.16 · Cited by 1,334 · 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
DNABERT: pre-trained bidirectional encoder representations from transformers model for DNA-language in genome
Ji, Y., Zhou, Z., Liu, H., & Davuluri, R. V. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab083 · Cited by 1,303 · see citing works
Self-citation
Deep learning: new computational modelling techniques for genomics
Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0122-6 · Cited by 1,229 · see citing works
Self-citation
A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response
Adamson, B., Norman, T. M., Jost, M., Cho, M. Y., Nuñez, J. K., Chen, Y., Villalta, J. E., Gilbert, L. A., Horlbeck, M. A., Hein, M. Y., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.048 · Cited by 1,193 · see citing works
Target identification and mechanism of action in chemical biology and drug discovery
Schenone, M., Dančík, V., Wagner, B. K., & Clemons, P. A. (2013) · Nature Chemical Biology · doi:10.1038/nchembio.1199 · Cited by 1,107 · see citing works
Pooled CRISPR screening with single-cell transcriptome readout.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096 · see citing works
Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes
Bray, M.-A., Singh, S., Han, H., Davis, C. T., Borgeson, B., Hartland, C., Kost-Alimova, M., Gustafsdottir, S. M., Gibson, C. C., & Carpenter, A. E. (2016) · Nature Protocols · doi:10.1038/nprot.2016.105 · Cited by 1,090 · see citing works
Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics
Stoeckius, M., Zheng, S., Houck-Loomis, B., Hao, S., Yeung, B. Z., Mauck, W. M., Smibert, P., & Satija, R. (2018) · Genome Biology · doi:10.1186/s13059-018-1603-1 · Cited by 1,046 · see citing works
Next-Generation Machine Learning for Biological Networks
Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018) · Cell · doi:10.1016/j.cell.2018.05.015 · Cited by 974 · see citing works
Correlating chemical sensitivity and basal gene expression reveals mechanism of action
Rees, M. G., Seashore-Ludlow, B. A., Cheah, J. H., Adams, D., Price, E. V., Gill, S., Javaid, S., Coletti, M. E., Jones, V., Bodycombe, N. E., et al. (2015) · Nature Chemical Biology · doi:10.1038/nchembio.1986 · Cited by 874 · see citing works
An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules
Basu, A. B., Bodycombe, N. E., Cheah, J. H., Price, E. V., Liu, K., Schaefer, G. I., Ebright, R. Y., Stewart, M. L., Ito, D., Wang, S. Y., et al. (2013) · Cell · doi:10.1016/j.cell.2013.08.003 · Cited by 820 · see citing works
Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq
Jaitin, D. A., Weiner, A., Yofe, I., Lara-Astiaso, D., Keren-Shaul, H., David, E., Salame, T. M., Tanay, A., van Oudenaarden, A., & Amit, I. (2016) · Cell · doi:10.1016/j.cell.2016.11.039 · Cited by 777 · see citing works
A community effort to assess and improve drug sensitivity prediction algorithms
Community, N. D., Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2877 · Cited by 761 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.00567 · Cited by 741 · see citing works
scGen predicts single-cell perturbation responses
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0494-8 · Cited by 661 · see citing works
Self-citation
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens
Gasperini, M., Hill, A. J., McFaline-Figueroa, J. L., Martin, B., Kim, S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J., Noble, W. S., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.029 · Cited by 613 · see citing works
Base-resolution models of transcription-factor binding reveal soft motif syntax
Avsec, Ž., Weilert, M., Shrikumar, A., Krueger, S., Alexandari, A., Dalal, K., Fropf, R., McAnany, C., Gagneur, J., Kundaje, A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00782-6 · Cited by 610 · see citing works
Massively multiplex single-cell Hi-C
Ramani, V., Deng, X.-X., Qiu, R., Gunderson, K., Steemers, F. J., Disteche, C., Noble, W. S., Duan, Z., & Shendure, J. (2016) · Nature Methods · doi:10.1038/nmeth.4155 · Cited by 545 · see citing works
Predicting drug response and synergy using a deep learning model of human cancer cells
Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F., Ma, J., & Ideker, T. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.09.014 · Cited by 544 · see citing works
Sequential regulatory activity prediction across chromosomes with convolutional neural networks
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research · doi:10.1101/gr.227819.117 · Cited by 537 · see citing works
DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Karimi, M., Wu, D. J., Wang, Z., & Shen, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz111 · Cited by 536 · see citing works
A compact review of molecular property prediction with graph neural networks
Wieder, O., Kohlbacher, S., Kuenemann, M., Garon, A., Ducrot, P., Seidel, T., & Langer, T. (2020) · Drug Discovery Today: Technologies · doi:10.1016/j.ddtec.2020.11.009 · Cited by 522 · see citing works
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
Studying and modelling dynamic biological processes using time-series gene expression data
Bar‐Joseph, Z., Gitter, A., & Simon, I. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3244 · Cited by 478 · see citing works
Machine Learning Methods in Drug Discovery
Patel, L., Shukla, T., Huang, X., Ussery, D. W., & Wang, S. (2020) · Molecules · doi:10.3390/molecules25225277 · Cited by 460 · see citing works
Explainable artificial intelligence model to predict acute critical illness from electronic health records
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448 · see citing works
Optimal number of features as a function of sample size for various classification rules
Hua, J., Xiong, Z., Lowey, J., Suh, E. B., & Dougherty, E. R. (2004) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti171 · Cited by 440 · see citing works
In Search of Lost Domain Generalization
Gulrajani, I., & Lopez-Paz, D. (2020) · arXiv · doi:10.48550/arxiv.2007.01434 · Cited by 440 · see citing works
A Benchmark for Interpretability Methods in Deep Neural NetworksPreprint
Hooker, S., Erhan, D., Kindermans, P.-J., & Kim, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10758 · Cited by 438 · see citing works
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper
Bagherian, M., Sabeti, E., Wang, K., Sartor, M. A., Nikolovska‐Coleska, Z., & Najarian, K. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz157 · Cited by 432 · see citing works
De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
Méndez‐Lucio, O., Baillif, B., Clevert, D.-A., Rouquié, D., & Wichard, J. (2020) · Nature Communications · doi:10.1038/s41467-019-13807-w · Cited by 422 · see citing works
Massively multiplex chemical transcriptomics at single-cell resolution
Srivatsan, S. R., McFaline-Figueroa, J. L., Ramani, V., Saunders, L., Cao, J., Packer, J., Pliner, H. A., Jackson, D. L., Daza, R. M., Christiansen, L., et al. (2020) · Science · doi:10.1126/science.aax6234 · Cited by 401 · see citing works
Exploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., & Weissman, J. S. (2019) · Science · doi:10.1126/science.aax4438 · Cited by 392 · see citing works
Image-based profiling for drug discovery: due for a machine-learning upgrade?
Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D., & Carpenter, A. E. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-020-00117-w · Cited by 387 · see citing works
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
Menden, M. P., Wang, D. Y. Q., Mason, M. J., Szalai, B., Bulusu, K. C., Guan, Y., Yu, T. V., Kang, J., Jeon, M., Wolfinger, R. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09799-2 · Cited by 360 · see citing works
Simultaneous profiling of 3D genome structure and DNA methylation in single human cells
Lee, D.-S., Luo, C., Zhou, J., Chandran, S., Rivkin, A., Bartlett, A., Nery, J. R., Fitzpatrick, C., O’Connor, C., Dixon, J. R., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0547-z · Cited by 340 · see citing works
Drug-disease and drug-drug interactions: systematic examination of recommendations in 12 UK national clinical guidelines
Dumbreck, S., Flynn, A., Nairn, M., Wilson, M., Treweek, S. P., Mercer, S. W., Alderson, P., Thompson, A., Payne, K., & Guthrie, B. (2015) · BMJ · doi:10.1136/bmj.h949 · Cited by 339 · see citing works
Using deep learning to annotate the protein universe
Bileschi, M. L., Belanger, D., Bryant, D. H., Sanderson, T., Carter, B., Sculley, D., Bateman, A., DePristo, M. A., & Colwell, L. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01179-w · Cited by 332 · see citing works
A community computational challenge to predict the activity of pairs of compounds
Community, N.-D., Bansal, M., Yang, J., Karan, C., Menden, M. P., Costello, J. C., Tang, H., Xiao, G., Li, Y., Allen, J., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3052 · Cited by 321 · see citing works
Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature
Chang, Y., Park, H., Yang, H., Lee, S., Lee, K.-Y., Kim, T. S., Jung, J., & Shin, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-27214-6 · Cited by 319 · see citing works
DeepPPI: Boosting Prediction of Protein–Protein Interactions with Deep Neural Networks
Du, X., Sun, S., Hu, C.-L., Yao, Y., Yan, Y., & Zhang, Y. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00028 · Cited by 286 · see citing works
Phenotypic landscape of intestinal organoid regeneration
Lukonin, I., Serra, D., Challet Meylan, L., Volkmann, K., Baaten, J., Zhao, R., Meeusen, S., Colman, K., Maurer, F., Stadler, M. B., et al. (2020) · Nature · doi:10.1038/s41586-020-2776-9 · Cited by 283 · see citing works
In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts
Yang, H., Sun, L., Li, W., Liu, G., & Tang, Y. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00030 · Cited by 282 · see citing works
Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion
Frangieh, C. J., Melms, J. C., Thakore, P. I., Geiger-Schuller, K. R., Ho, P., Luoma, A. M., Cleary, B., Jerby-Arnon, L., Malu, S., Cuoco, M. S., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00779-1 · Cited by 276 · see citing works
Cell type prioritization in single-cell data
Skinnider, M. A., Squair, J. W., Kathe, C., Anderson, M. A., Gautier, M., Matson, K. J. E., Milano, M., Hutson, T. H., Barraud, Q., Phillips, A. A., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0605-1 · Cited by 274 · see citing works
Integrative Methods and Practical Challenges for Single-Cell Multi-omics
Ma, A., McDermaid, A., Xu, J., Chang, Y., & Ma, Q. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.02.013 · Cited by 272 · see citing works
On the design of CRISPR-based single-cell molecular screens
Hill, A. J., McFaline-Figueroa, J. L., Starita, L. M., Gasperini, M. J., Matreyek, K. A., Packer, J., Jackson, D., Shendure, J., & Trapnell, C. (2018) · Nature Methods · doi:10.1038/nmeth.4604 · Cited by 262 · see citing works
Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders
Way, G. P., & Greene, C. S. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0008 · Cited by 256 · see citing works
PharmacoGx: an R package for analysis of large pharmacogenomic datasets
Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H. M., Gendoo, D. M. A., Großmann, P., et al. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv723 · Cited by 255 · see citing works
Targeted Perturb-seq enables genome-scale genetic screens in single cells
Schraivogel, D., Gschwind, A. R., Milbank, J. H., Leonce, D. R., Jakob, P., Mathur, L., Korbel, J. O., Merten, C. A., Velten, L., & Steinmetz, L. M. (2020) · Nature Methods · doi:10.1038/s41592-020-0837-5 · Cited by 245 · see citing works
A curated database reveals trends in single-cell transcriptomics
Svensson, V., da Veiga Beltrame, E., & Pachter, L. (2020) · Database · doi:10.1093/database/baaa073 · Cited by 223 · see citing works
DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery
Ye, C., Ho, D. J., Neri, M., Yang, C., Kulkarni, T., Randhawa, R., Henault, M., Mostacci, N., Farmer, P., Renner, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06500-x · Cited by 222 · see citing works
Enter the Matrix: Factorization Uncovers Knowledge from Omics
Stein-O’Brien, G., Arora, R., Culhane, A. C., Favorov, A. V., Garmire, L. X., Greene, C. S., Goff, L. A., Li, Y., Ngom, A., Ochs, M. F., et al. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.07.003 · Cited by 221 · see citing works
Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation
Subramanian, A., Sidhom, E.-H., Emani, M., Vernon, K., Sahakian, N., Zhou, Y., Kost-Alimova, M., Slyper, M., Waldman, J., Dionne, D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13382-0 · Cited by 211 · see citing works
Quantifying drug combination synergy along potency and efficacy axes
Meyer, C. T., Wooten, D. J., Paudel, B. B., Bauer, J., Hardeman, K. N., Westover, D., Lovly, C. M., Harris, L. A., Tyson, D. R., & Quaranta, V. (2019) · Cell Systems · doi:10.1016/j.cels.2019.01.003 · Cited by 202 · see citing works
Using neural networks for reducing the dimensions of single-cell RNA-Seq data
Lin, C., Jain, S., Kim, H., & Bar-Joseph, Z. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx681 · Cited by 197 · see citing works
Dr.VAE: improving drug response prediction via modeling of drug perturbation effects
Rampášek, L., Hidru, D., Smirnov, P., Haibe‐Kains, B., & Goldenberg, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz158 · Cited by 174 · see citing works
Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action
McFarland, J. M., Paolella, B. R., Warren, A., Geiger-Schuller, K., Shibue, T., Rothberg, M., Kuksenko, O., Colgan, W. N., Jones, A., Chambers, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17440-w · Cited by 173 · see citing works
Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq
Qiu, Q., Hu, P., Qiu, X., Govek, K. W., Cámara, P. G., & Wu, H. (2020) · Nature Methods · doi:10.1038/s41592-020-0935-4 · Cited by 172 · see citing works
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the UglyPreprint
Xian, Y., Lampert, C. H., Schiele, B., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00600 · Cited by 171 · see citing works
A single-cell tumor immune atlas for precision oncologyPreprint
Nieto, P., Elosua-Bayés, M., Trincado, J. L., Marchese, D., Massoni-Badosa, R., Salvany, M., Henriques, A. R., Nieto, J. C., Aguilar-Fernández, S., Mereu, E., et al. (2021) · Genome Research · doi:10.1101/gr.273300.120 · Cited by 169 · see citing works
Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs
Jost, M., Santos, D. A., Saunders, R. A., Horlbeck, M. A., Hawkins, J. S., Scaria, S. M., Norman, T. M., Hussmann, J. A., Liem, C. R., Gross, C. A., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0387-5 · Cited by 164 · see citing works
Sci-fate characterizes the dynamics of gene expression in single cells
Cao, J., Zhou, W. Y., Steemers, F. J., Trapnell, C., & Shendure, J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0480-9 · Cited by 162 · see citing works
Single-cell analyses to tailor treatments
Shalek, A. K., & Benson, M. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aan4730 · Cited by 157 · see citing works
PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies
Smirnov, P., Kofia, V., Maru, A., Freeman, M., Ho, C., El-Hachem, N., Adam, G.-A., Ba-alawi, W., Safikhani, Z., & Haibe-Kains, B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx911 · Cited by 155 · see citing works
Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model
Fröhlich, F., Kessler, T., Weindl, D., Shadrin, A., Schmiester, L., Hache, H., Muradyan, A., Schütte, M., Lim, J.-H., Heinig, M., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.013 · Cited by 143 · see citing works
Self-citation
MARS: discovering novel cell types across heterogeneous single-cell experiments
Brbić, M., Žitnik, M., Wang, S., Pisco, A. O., Altman, R. B., Darmanis, S., & Leskovec, J. (2020) · Nature Methods · doi:10.1038/s41592-020-00979-3 · Cited by 141 · see citing works
The Impact of Heterogeneity on Single-Cell Sequencing
Goldman, S. L., MacKay, M. J., Afshinnekoo, E., Melnick, A. M., Wu, S., & Mason, C. E. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00008 · Cited by 139 · see citing works
Zero-Shot Learning via Class-Conditioned Deep Generative Models
Wang, W., Pu, Y., Verma, V. K., Fan, K., Zhang, Y., Chen, C., Rai, P., & Carin, L. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11600 · Cited by 139 · see citing works
Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
Fortelny, N., & Bock, C. (2020) · Genome Biology · doi:10.1186/s13059-020-02100-5 · Cited by 135 · see citing works
Conditional out-of-distribution generation for unpaired data using transfer VAE
Lotfollahi, M., Naghipourfar, M., Theis, F. J., & Wolf, F. A. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa800 · Cited by 130 · see citing works
Self-citation
CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy
Yuan, B., Shen, C., Luna, A., Korkut, A., Marks, D. S., Ingraham, J., & Sander, C. (2020) · Cell Systems · doi:10.1016/j.cels.2020.11.013 · Cited by 128 · see citing works
Advances and Challenges in Computational Target Prediction
Sydow, D., Burggraaff, L., Szengel, A., van Vlijmen, H., IJzerman, A. P., van Westen, G. J. P., & Volkamer, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00832 · Cited by 127 · see citing works
Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins
Gehring, J., Hwee Park, J., Chen, S., Thomson, M., & Pachter, L. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0372-z · Cited by 122 · see citing works
Predicting compound activity from phenotypic profiles and chemical structures
Moshkov, N., Becker, T., Yang, K., Horváth, P., Dančík, V., Wagner, B. K., Clemons, P. A., Singh, S., Carpenter, A. E., & Caicedo, J. C. (2023) · Nature Communications · doi:10.1038/s41467-023-37570-1 · Cited by 116 · see citing works
Single-cell genomic approaches for developing the next generation of immunotherapies
Yofe, I., Dahan, R., & Amit, I. (2020) · Nature Medicine · doi:10.1038/s41591-019-0736-4 · Cited by 106 · see citing works
Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations
Shin, D., Lee, W., Lee, J. H., & Bang, D. (2019) · Science Advances · doi:10.1126/sciadv.aav2249 · Cited by 101 · see citing works
Model-based understanding of single-cell CRISPR screening
Duan, B., Zhou, C., Zhu, C., Yu, Y., Li, G., Zhang, S., Zhang, C., Ye, X., Ma, H., Qu, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10216-x · Cited by 98 · see citing works
Generative adversarial networks simulate gene expression and predict perturbations in single cellsPreprint
Ghahramani, A., Watt, F. M., & Luscombe, N. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/262501 · Cited by 98 · see citing works
scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens
Yang, L., Zhu, Y., Yu, H., Cheng, X., Chen, S., Chu, Y., Huang, H., Zhang, J., & Li, W. (2020) · Genome Biology · doi:10.1186/s13059-020-1928-4 · Cited by 94 · see citing works
Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq
Zhao, W., Dovas, A., Spinazzi, E. F., Levitin, H. M., Banu, M. A., Upadhyayula, P. S., Sudhakar, T. D., Marie, T., Otten, M. L., Sisti, M. B., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00894-y · Cited by 90 · see citing works
A Highly Scalable Method for Joint Whole-Genome Sequencing and Gene-Expression Profiling of Single Cells
Zachariadis, V., Cheng, H., Andrews, N., & Enge, M. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.09.025 · Cited by 90 · see citing works
Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays
Movva, R., Greenside, P., Marinov, G. K., Nair, S., Shrikumar, A., & Kundaje, A. B. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218073 · Cited by 87 · see citing works
Identification of somatic mutations in single cell DNA-seq using a spatial model of allelic imbalance
Luquette, L. J., Bohrson, C. L., Sherman, M. A., & Park, P. J. (2019) · Nature Communications · doi:10.1038/s41467-019-11857-8 · Cited by 84 · see citing works
Mapping human cell phenotypes to genotypes with single-cell genomics
Camp, J. G., Platt, R. J., & Treutlein, B. (2019) · Science · doi:10.1126/science.aax6648 · Cited by 83 · see citing works
Effective gene expression prediction from sequence by integrating long-range interactions
(2021) · doi:10.1101/2021.04.07.438649 · Cited by 82 · see citing works
Using Deep Learning to Annotate the Protein UniversePreprint
Bileschi, M. L., Belanger, D., Bryant, D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/626507 · Cited by 81 · see citing works
CellOracle: Dissecting cell identity via network inference and in silico gene perturbation
Kamimoto, K., Hoffmann, C. M., & Morris, S. A. (2020) · doi:10.1101/2020.02.17.947416 · Cited by 81 · see citing works
Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology
Ietswaart, R., Arat, S., Chen, A. X., Farahmand, S., Kim, B., DuMouchel, W., Armstrong, D., Fekete, A., Sutherland, J. J., & Urbán, L. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.102837 · Cited by 79 · see citing works
Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors
Mitra, A., Mukherjee, U. K., Harding, T., Jang, J. S., Stessman, H. A. F., Li, Y., Abyzov, A. S., Jen, J., Kumar, S. K., Rajkumar, S. V., et al. (2015) · Leukemia · doi:10.1038/leu.2015.361 · Cited by 71 · see citing works
Single-Cell Techniques and Deep Learning in Predicting Drug Response
Wu, Z., Lawrence, P. J., Ma, A., Zhu, J., Xu, D., & Ma, Q. (2020) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2020.10.004 · Cited by 68 · see citing works
A multiresolution framework to characterize single-cell state landscapes
Mohammadi, S., Dávila-Velderrain, J., & Kellis, M. (2020) · Nature Communications · doi:10.1038/s41467-020-18416-6 · Cited by 64 · see citing works
Patient-derived xenograft models—the future of personalised cancer treatment
Bhimani, J., Ball, K., & Stebbing, J. (2020) · British Journal of Cancer · doi:10.1038/s41416-019-0678-0 · Cited by 64 · see citing works
Training neural networks on high-dimensional data using random projection
Wójcik, P. I., & Kurdziel, M. (2018) · Pattern Analysis and Applications · doi:10.1007/s10044-018-0697-0 · Cited by 61 · see citing works
Single-cell sequencing of genomic DNA resolves sub-clonal heterogeneity in a melanoma cell line
Velazquez-Villarreal, E. I., Maheshwari, S., Sorenson, J. M., Fiddes, I. T., Kumar, V., Yin, Y., Webb, M. G., Catalanotti, C., Grigorova, M., Edwards, P. A., et al. (2020) · Communications Biology · doi:10.1038/s42003-020-1044-8 · Cited by 61 · see citing works
RNA timestamps identify the age of single molecules in RNA sequencing
Rodriques, S. G., Chen, L. M., Liu, S., Zhong, E. D., Scherrer, J. R., Boyden, E. S., & Chen, F. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0704-z · Cited by 55 · see citing works
PharmDock: a pharmacophore-based docking program
Hu, B., & Lill, M. A. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-14 · Cited by 53 · see citing works
Zero-Shot Learning via Class-Conditioned Deep Generative ModelsPreprint
Wang, W., Pu, Y., Verma, V. K., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05820 · Cited by 50 · see citing works
Uncovering axes of variation among single-cell cancer specimens
Chen, W. S., Zivanovic, N., van Dijk, D., Wolf, G., Bodenmiller, B., & Krishnaswamy, S. (2020) · Nature Methods · doi:10.1038/s41592-019-0689-z · Cited by 48 · see citing works
DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity
Anchang, B., Davis, K. L., Fienberg, H. G., Williamson, B. D., Bendall, S. C., Karacosta, L. G., Tibshirani, R. J., Nolan, G. P., & Plevritis, S. K. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711365115 · 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
Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discoveryPreprint
Cuccarese, M. F., Earnshaw, B. A., Heiser, K., Fogelson, B., Davis, C. T., McLean, P., Gordon, H. B., Skelly, K.-R., Weathersby, F. L., Rodic, V., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.02.233064 · Cited by 47 · see citing works
Novel Transformer Networks for Improved Sequence Labeling in genomics
Clauwaert, J., & Waegeman, W. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3035021 · Cited by 47 · see citing works
Linking drug target and pathway activation for effective therapy using multi-task learning
Yang, M., Simm, J., Lam, C. C., Zakeri, P., van Westen, G. J. P., Moreau, Y., & Sáez-Rodríguez, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-25947-y · Cited by 46 · see citing works
Ontology application and use at the ENCODE DCC
Malladi, V. S., Erickson, D. T., Podduturi, N. R., Rowe, L. D., Chan, E. T., Davidson, J. M., Hitz, B. C., Ho, M., Lee, B. T., Miyasato, S. R., et al. (2015) · Database · doi:10.1093/database/bav010 · Cited by 45 · see citing works
Query to reference single-cell integration with transfer learningPreprint
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Avsec, Ž., Misharin, A. V., & Theis, F. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.16.205997 · Cited by 38 · see citing works
Self-citation
Deep learning at base-resolution reveals motif syntax of the cis-regulatory codePreprint
Avsec, Ž., Weilert, M., Shrikumar, A., Alexandari, A. M., Krueger, S., Dalal, K., Fropf, R., McAnany, C. E., Gagneur, J., Kundaje, A., et al. (2019) · bioRxiv · doi:10.1101/737981 · Cited by 36 · see citing works
The Roles of Common Variation and Somatic Mutation in Cancer Pharmacogenomics
Chan, T., Chin, Y. M., & Low, S. (2019) · Oncology and Therapy · doi:10.1007/s40487-018-0090-6 · Cited by 35 · see citing works
Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign
Chen, S., Rivaud, P., Park, J., Tsou, T., Charles, E. J., Haliburton, J. R., Pichiorri, F., & Thomson, M. W. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005990117 · Cited by 35 · see citing works
Sfaira accelerates data and model reuse in single cell genomics
Fischer, D. S., Dony, L., König, M., Moeed, A., Zappia, L., Heumos, L., Tritschler, S., Holmberg, O., Aliee, H., & Theis, F. J. (2021) · Genome Biology · doi:10.1186/s13059-021-02452-6 · Cited by 33 · see citing works
Self-citation
A Single-Cell Tumor Immune Atlas for Precision OncologyPreprint
Nieto, P., Elosua-Bayés, M., Trincado, J. L., Marchese, D., Massoni-Badosa, R., Salvany, M., Henriques, A. R., Mereu, E., Moutinho, C., Ruiz, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.26.354829 · Cited by 30 · see citing works
Zero-shot Learning via Simultaneous Generating and LearningPreprint
Yu, H., & Lee, B.-H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.09446 · Cited by 29 · see citing works
Emerging deep learning methods for single‐cell RNA‐seq data analysis
Zheng, J., & Wang, K. (2019) · Quantitative Biology · doi:10.1007/s40484-019-0189-2 · Cited by 28 · see citing works
Gene Perturbation Atlas (GPA): a single-gene perturbation repository for characterizing functional mechanisms of coding and non-coding genes
Xiao, Y., Gong, Y., Lv, Y., Lan, Y., Hu, J. R., Li, F., Xu, J., Bai, J., Deng, Y., Liu, L., et al. (2015) · Scientific Reports · doi:10.1038/srep10889 · Cited by 26 · see citing works
PmVAE: learning interpretable single-cell representations with pathway modules
Gut, G., Stark, S. G., Rätsch, G., & Davidson, N. R. (2021) · doi:10.1101/2021.01.28.428664 · Cited by 24 · see citing works
Predicting compound activity from phenotypic profiles and chemical structuresPreprint
Moshkov, N., Becker, T., Yang, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.15.422887 · Cited by 24 · see citing works
Causal network perturbations for instance-specific analysis of single cell and disease samples
Buschur, K. L., Chikina, M. D., & Benos, P. V. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz949 · Cited by 23 · see citing works
Therapeutics Data Commons: Machine Learning Datasets and Tasks for TherapeuticsPreprint
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., & Žitnik, M. (2021) · arXiv (Cornell University) · Cited by 22 · see citing works
Prediction of Side Effects Using Comprehensive Similarity Measures
Seo, S., Lee, T., Kim, M., & Yoon, Y. (2020) · BioMed Research International · doi:10.1155/2020/1357630 · Cited by 21 · see citing works
Fast searches of large collections of single-cell data using scfind
Lee, J. T. H., Patikas, N., Kiselev, V. Y., & Hemberg, M. (2021) · Nature Methods · doi:10.1038/s41592-021-01076-9 · Cited by 17 · see citing works
Deconvolution of Cell Type-Specific Drug Responses in Human Tumor Tissue with Single-Cell RNA-seqPreprint
Zhao, W., Dovas, A., Spinazzi, E. F., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.22.056341 · Cited by 12 · see citing works
Massively parallel phenotyping of variant impact in cancer with Perturb-seq reveals a shift in the spectrum of cell states induced by somatic mutationsPreprint
Ursu, O., Neal, J. T., Shea, E. K., Thakore, P. I., Jerby‐Arnon, L., Nguyễn, L., Dionne, D. A., Diaz, C., Bauman, J., Mosaad, M. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.16.383307 · Cited by 11 · see citing works
Novel transformer networks for improved sequence labeling in genomicsPreprint
Clauwaert, J., & Waegeman, W. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/836163 · Cited by 11 · see citing works
Sfaira accelerates data and model reuse in single cell genomicsPreprint
Fischer, D. S., Dony, L., König, M. F., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.16.419036 · Cited by 10 · see citing works
Interpretable Machine Learning for Perturbation BiologyPreprint
Yuan, B., Shen, C., Luna, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/746842 · Cited by 8 · see citing works
Causal Imputation via Synthetic InterventionsPreprint
Squires, C., Shen, D., Agarwal, A., Devavrat Shah, B., & Uhler, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03127 · Cited by 8 · see citing works
DrugOrchestra: Jointly predicting drug response, targets, and side effects via deep multi-task learningPreprint
Jiang, Y., Rensi, S., Wang, S., & Altman, R. B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.17.385757 · Cited by 8 · see citing works
Disentangling latent representations of single cell RNA-seq experimentsPreprint
Kimmel, J. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.04.972166 · Cited by 7 · see citing works
Mapping multicellular programs from single-cell profilesPreprint
Jerby‐Arnon, L., & Regev, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.11.245472 · Cited by 7 · see citing works
Comprehensive benchmarking of single cell RNA sequencing technologies for characterizing cellular perturbationPreprint
Verboom, K., Assefa, A. T., Yigit, N., Anckaert, J., Vandamme, N., Rombaut, D., Saeys, Y., Thas, O., Speleman, F., Durinck, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.25.396523 · Cited by 2 · see citing works
DeepCellState: an autoencoder-based framework for predicting cell type-specific transcriptional states induced by drug treatmentPreprint
Umarov, R., Li, Y., & Arner, E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.14.422792 · Cited by 1 · see citing works
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

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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
“However, current 47 machine-learning-based methods in single-cell research are rather scattered, with specific models 48 dedicated to distinct analysis tasks [24, 25, 26].”
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 · see citing works
“3).”
Self-citation
Dissecting cell identity via network inference and in silico gene perturbation
Kamimoto, K., Stringa, B., Hoffmann, C. M., Jindal, K., Solnica-Krezel, L., & Morris, S. A. (2023) · Nature · doi:10.1038/s41586-022-05688-9 · Cited by 677 · see citing works
Applications of single-cell RNA sequencing in drug discovery and development
de Sande, B. V., Lee, J. S., Mutasa-Gottgens, E., Naughton, B. J., Bacon, W., Manning, J. R., Wang, Y., Pollard, J. R., Mendez, M., Hill, J., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00688-4 · Cited by 441 · see citing works
Impact of the Human Cell Atlas on medicine
Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A., & Regev, A. (2022) · Nature Medicine · doi:10.1038/s41591-022-02104-7 · Cited by 222 · see citing works
scPerturb: harmonized single-cell perturbation data
Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194 · 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
Self-citation
Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas
Rood, J. E., Hupalowska, A., & Regev, A. (2024) · Cell · doi:10.1016/j.cell.2024.07.035 · Cited by 139 · see citing works
DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data
Jerby‐Arnon, L., & Regev, A. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01288-0 · Cited by 116 · see citing works
Single-cell profiling to explore pancreatic cancer heterogeneity, plasticity and response to therapy
Bärthel, S., Falcomatà, C., Rad, R., Theis, F. J., & Saur, D. (2023) · Nature Cancer · doi:10.1038/s43018-023-00526-x · Cited by 101 · see citing works
Self-citation
Mapping spatial organization and genetic cell-state regulators to target immune evasion in ovarian cancer
Yeh, C. Y., Aguirre, K., Laveroni, O., Kim, S., Wang, A., Liang, B., Zhang, X., Han, L. M., Valbuena, R., Bassik, M. C., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01943-5 · Cited by 91 · see citing works
Predicting cellular responses to perturbation across diverse contexts with State
Adduri, A. K., Gautam, D., Bevilacqua, B., Naghipourfar, M., Imran, A., Shah, R., Teyssier, N., Verma, R., Carpenter, C., Eraslan, B., et al. (2026) · Cell · doi:10.1016/j.cell.2026.07.052 · Cited by 100 · see citing works
Ex vivo tissue perturbations coupled to single-cell RNA-seq reveal multilineage cell circuit dynamics in human lung fibrogenesis
Lang, N. J., Gote-Schniering, J., Porras-Gonzalez, D., Yang, L., Sadeleer, L. D., Jentzsch, R. C., Shitov, V. A., Zhou, S., Ansari, M., Agami, A., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adh0908 · Cited by 89 · see citing works
Self-citation
The future of rapid and automated single-cell data analysis using reference mapping
Lotfollahi, M., Hao, Y., Theis, F. J., & Satija, R. (2024) · Cell · doi:10.1016/j.cell.2024.03.009 · Cited by 77 · see citing works
Self-citation
Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level
Qi, R., & Zou, Q. (2023) · Research · doi:10.34133/research.0050 · Cited by 72 · see citing works
Transcriptional vulnerabilities of striatal neurons in human and rodent models of Huntington’s disease
Matsushima, A., Pineda, S. S., Crittenden, J. R., Lee, H., Galani, K., Mantero, J. C., Tombaugh, G. C., Kellis, M., Heiman, M., & Graybiel, A. M. (2023) · Nature Communications · doi:10.1038/s41467-022-35752-x · Cited by 72 · see citing works
Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses
Ramos Zapatero, M., Tong, A., Opzoomer, J. W., O’Sullivan, R., Cardoso Rodriguez, F., Sufi, J., Vlckova, P., Nattress, C., Qin, X., Claus, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.005 · Cited by 72 · see citing works
Optimal transport for single-cell and spatial omics
Bunne, C., Schiebinger, G., Krause, A., Regev, A., & Cuturi, M. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00334-2 · Cited by 71 · see citing works
A mini-review on perturbation modelling across single-cell omic modalities
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Enabling Single‐Cell Drug Response Annotations from Bulk RNA‐Seq Using SCAD
Zheng, Z., Chen, J., Chen, X., Huang, L., Xie, W., Lin, Q., Li, X., & Wong, K. (2023) · Advanced Science · doi:10.1002/advs.202204113 · Cited by 46 · see citing works
Large-scale, spatially resolved panoramic CRISPR screening in native tissue environments using Perturb-DBiT
Baysoy, A., Tian, X., Renauer, P. A., Zhang, F., Bai, Z., Shi, H., Yang, M., Zhang, D., Liu, M., Li, H., et al. (2026) · Nature Biotechnology · doi:10.1038/s41587-026-03127-y · Cited by 91 · see citing works
Spatially Resolved in vivo CRISPR Screen Sequencing via Perturb-DBiTPreprint
Baysoy, A., Tian, X., Zhang, F., Renauer, P. A., Bai, Z., Shi, H., Li, H., Tao, B., Yang, M., Enninful, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.18.624106 · Cited by 45 · see citing works
Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review
Mohammed, M. A., Abdulkareem, K. H., Dinar, A. M., & García-Zapirain, B. (2023) · Diagnostics · doi:10.3390/diagnostics13040664 · Cited by 39 · see citing works
Proteomic landscape of Alzheimer’s disease: emerging technologies, advances and insights (2021 – 2025)
Yarbro, J. M., Shrestha, H. K., Wang, Z., Zhang, X., Zaman, M., Chu, M., Wang, X., Yu, G., & Peng, J. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-025-00874-5 · Cited by 41 · see citing works
AI-empowered perturbation proteomics for complex biological systems
Qian, L., Sun, R., Aebersold, R. H., Bühlmann, P., Sander, C., & Guo, T. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100691 · Cited by 31 · see citing works
What is a cell type?
Fleck, J. S., Camp, J. G., & Treutlein, B. (2023) · Science · doi:10.1126/science.adf6162 · Cited by 30 · see citing works
scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism
Jiang, Q., Chen, S., Chen, X., & Jiang, R. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae265 · Cited by 29 · see citing works
PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization
Wei, Z., Si, D., Duan, B., Gao, Y., Yu, Q., Zhang, Z., Guo, L., & Liu, Q. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae858 · Cited by 30 · 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
A Systematic Comparison of Single-Cell Perturbation Response Prediction Models
Li, L., You, Y., Fu, Y., Liao, W., Fan, X., Lu, S., Cao, Y., Li, B., Ren, W., Kong, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.23.630036 · Cited by 25 · see citing works
“Early approaches focused on predicting cellular responses in cell types absent from experimental training data or responses to combinations of previously tested perturbations ( 11–13 ).”
Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis
Alessandrì, L., Ratto, M. L., Contaldo, S. G., Beccuti, M., Cordero, F., Arigoni, M., & Calogero, R. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222312755 · Cited by 24 · see citing works
A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data
Maeser, D., Zhang, W., Huang, Y., & Huang, R. S. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102745 · Cited by 25 · see citing works
Adapting systems biology to address the complexity of human disease in the single-cell era
Fischer, D. S., Villanueva, M. A., Winter, P. S., & Shalek, A. K. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00821-6 · Cited by 30 · see citing works
High-Resolution Bacterial Cytological Profiling Reveals Intrapopulation Morphological Variations upon Antibiotic Exposure
Samernate, T., Htoo, H. H., Sugie, J., Chavasiri, W., Pogliano, J., Chaikeeratisak, V., & Nonejuie, P. (2023) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.01307-22 · Cited by 21 · see citing works
Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors
Zhang, W., Maeser, D., Lee, A. M., Huang, Y., Gruener, R. F., Abdelbar, I. G., Jena, S., Patel, A. G., & Huang, R. S. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-3005 · Cited by 21 · see citing works
Challenges and considerations for single-cell and spatially resolved transcriptomics sample collection during spaceflight
Overbey, E. G., Das, S., Cope, H., Madrigal, P., Andrusivová, Ž., Frapard, S., Klotz, R., Bezdan, D., Gupta, A. E., Scott, R. T., et al. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100325 · Cited by 22 · see citing works
Weakly supervised learning uncovers phenotypic signatures in single-cell dataPreprint
Litinetskaya, A., Hediyeh-zadeh, S., Moinfar, A. A., Lotfollahi, M., & Theis, F. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.29.605625 · Cited by 18 · see citing works
Self-citation
Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications
Sun, J., Kulandaisamy, A., Liu, J., Hu, K., Gromiha, M. M., & Zhang, Y. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.01.036 · Cited by 19 · see citing works
Machine learning for metabolomics research in drug discovery
Martinelli, D. D. (2023) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2023.100101 · Cited by 19 · see citing works
Optimal distance metrics for single-cell RNA-seq populationsPreprint
Ji, Y., Green, T. D., Peidli, S., Bahrami, M., Liu, M., Zappia, L., Hrovatin, K., Sander, C., & Theis, F. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.26.572833 · Cited by 18 · see citing works
Self-citation
A Final Frontier in Environment-Genome Interactions? Integrated, Multi-Omic Approaches to Predictions of Non-Communicable Disease Risk
Noble, A. J., Purcell, R. V., Adams, A. T., Lam, Y. K., Ring, P. M., Anderson, J. R., & Osborne, A. J. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.831866 · Cited by 18 · see citing works
MultiCPA: Multimodal Compositional Perturbation Autoencoder
Inecik, K., Uhlmann, A., Lotfollahi, M., & Theis, F. (2022) · doi:10.1101/2022.07.08.499049 · Cited by 16 · see citing works
Self-citation
Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond
Gao, Y., Wei, Z., Dong, K., Chen, K., Yang, J., Chuai, G., & Liu, Q. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00698-1 · Cited by 17 · see citing works
Machine learning to dissect perturbations in complex cellular systems
Monfort‐Lanzas, P., Rungger, K., Madersbacher, L., & Hackl, H. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.02.028 · Cited by 17 · see citing works
SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation
Jolasun, Y., Song, K., Zheng, Y., Wang, J., Fonseca, G. J., Eidelman, D. H., & Ding, J. (2025) · Nature Communications · doi:10.1038/s41467-025-66162-4 · Cited by 16 · see citing works
Quantitative systems pharmacology of the eye: Tools and data for ocular QSP
Kuepfer, L., Fuellen, G., & Stahnke, T. (2023) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12918 · Cited by 14 · see citing works
A systematic comparison of computational methods for expression forecasting
Kernfeld, E., Yang, Y., Weinstock, J. S., Battle, A., & Cahan, P. (2023) · doi:10.1101/2023.07.28.551039 · Cited by 15 · 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
CellDrift: inferring perturbation responses in temporally sampled single-cell data
Jin, K., Schnell, D., Li, G., Salomonis, N., Prasath, V. B. S., Szczesniak, R., & Aronow, B. J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac324 · Cited by 11 · see citing works
Redefining cellular reprogramming with advanced genomic technologies
Morris, S. A. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00899-y · Cited by 13 · see citing works
Interpretation, extrapolation and perturbation of single cells
Dimitrov, D., Schrod, S., Rohbeck, M., & Stegle, O. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-025-00920-4 · Cited by 18 · see citing works
Improving reduced-order models through nonlinear decoding of projection-dependent outputs
Zdybał, K., Parente, A., & Sutherland, J. C. (2023) · Patterns · doi:10.1016/j.patter.2023.100859 · Cited by 10 · see citing works
Single-cell omics analysis with genome-scale metabolic modeling
Chen, Y., Gustafsson, J., Yang, J., Nielsen, J., & Kerkhoven, E. J. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103078 · Cited by 11 · see citing works
Systems immunology: When systems biology meets immunology
Alfonso‐González, L., Fernández, F. J., & Vega, M. C. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1630488 · Cited by 11 · see citing works
Regulating epithelial-mesenchymal plasticity from 3D genome organization
Pang, Q. Y., Chiu, Y.-C., & Huang, R. Y. (2024) · Communications Biology · doi:10.1038/s42003-024-06441-w · Cited by 9 · see citing works
Advancing single-cell omics and cell-based therapeutics with quantum computing
Bose, A., Rhrissorrakrai, K., Utro, F., Parida, L., Basu, S., Born, J., Capponi, S., Chalkia, D., Chan, T. A.-T., Doğa, H., et al. (2026) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-025-00918-0 · Cited by 10 · see citing works
Foundation Models Improve Perturbation Response PredictionPreprint
Cole, E., Huizing, G.-J., Addagudi, S., Ho, N., Hasanaj, E., Kuijs, M., Johnstone, T., Carilli, M., Davi, A., Ellington, C. N., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.18.706454 · Cited by 13 · see citing works
Explainable modeling of single-cell perturbation data using attention and sparse dictionary learning
Xu, Y., Fleming, S., Tegtmeyer, M., McCarroll, S. A., & Babadi, M. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101245 · Cited by 8 · see citing works
Decoding cellular communication: An information theoretic perspective on cytokine and endocrine signaling
Schaper, F., Jetka, T., & Dittrich, A. (2022) · Current Opinion in Endocrine and Metabolic Research · doi:10.1016/j.coemr.2022.100351 · Cited by 8 · see citing works
Trellis Single-Cell Screening Reveals Stromal Regulation of Patient-Derived Organoid Drug ResponsesPreprint
Zapatero, M. R., Tong, A., Sufi, J., Vlckova, P., Rodriguez, F. C., Nattress, C. B., Qin, X., Hochhauser, D., Krishnaswamy, S., & Tape, C. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.19.512668 · Cited by 7 · see citing works
CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data
Wu, Y., Liu, J., Liu, S., Xiao, Y., Zhang, S., & Li, L. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf126 · Cited by 6 · see citing works
Computational modeling of single-cell dynamics data
Guo, W., Chen, Z., & Gu, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf305 · Cited by 7 · see citing works
Deep Learning for Clustering Single-cell RNA-seq Data
Zhu, Y., Bai, L., Ning, Z., Fu, W., Liu, J., Jiang, L., Fei, S., Gong, S., Lu, L., Deng, M., et al. (2022) · Current Bioinformatics · doi:10.2174/1574893618666221130094050 · Cited by 6 · see citing works
Transcription factor network dynamics during the commitment to oncogene-induced senescence
Vasilopoulos, T., & Martínez‐Zamudio, R. I. (2024) · Frontiers in Epigenetics and Epigenomics · doi:10.3389/freae.2024.1423454 · Cited by 5 · see citing works
A statistical framework for inferring genetic requirements from embryo-scale single-cell sequencing experimentsPreprint
Duran, M., Barkan, E. R., Tresenrider, A. M., Lee, H., Friedman, R. Z., Lammers, N. C., Colón, M., Franks, J. M., Ewing, B., Kimelman, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.03.646654 · Cited by 6 · see citing works
Fusion of spatiotemporal and network models to prioritize multiscale effects in single-cell perturbations
Egbon, O. A., Hickey, J. W., & Anchang, B. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf277 · Cited by 5 · see citing works
Building a two-way street between cell biology and machine learning
Uhler, C. (2024) · Nature Cell Biology · doi:10.1038/s41556-023-01279-6 · Cited by 5 · see citing works
Genomic Sequencing: Techniques, Advancements, and the Path Ahead
Patwekar, M. F., Patwekar, F., Badarinath, A. V., Billah, A. A. M., Gorijavolu, V., Krishnan, K., Shanmugasundaram, P., Prasad, P. D., & Kazi, A. A. (2025) · Journal of Bio-X Research · doi:10.34133/jbioxresearch.0046 · Cited by 5 · see citing works
Modeling chimeric antigen receptor response at the single-cell level with conditional optimal transport
Driessen, A., Born, J., Castellanos-Rueda, R., Reddy, S. T., & Rapsomaniki, M. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101591 · Cited by 4 · see citing works
Conditional Monge Gap enables generalizable single-cell perturbation modelling
Driessen, A., Rajwade, D. A., Harsanyi, B., Rapsomaniki, M., & Born, J. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01242-8 · Cited by 4 · see citing works
Exploring the role of transcriptional and post‐transcriptional processes in mRNA co‐expression
García‐Blay, Ó., Verhagen, P. G. A., Martin, B., & Hansen, M. M. K. (2023) · BioEssays · doi:10.1002/bies.202300130 · Cited by 4 · see citing works
Charting oncogenicity of genes and variants across lineages via multiplexed screens in teratomas
Parekh, U., McDonald, D., Dailamy, A., Wu, Y., Cordes, T., Zhang, K., Tipps, A. M. P., Metallo, C. M., & Mali, P. (2021) · iScience · doi:10.1016/j.isci.2021.103149 · Cited by 4 · see citing works
From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes?
Piochi, L. F., Gaspar, A. T., Rosário‐Ferreira, N., Preto, A. J., & Moreira, I. S. (2022) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2022.05.006 · Cited by 4 · see citing works
Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes
Klumpe, H. E., Lugagne, J., Khalil, A. S., & Dunlop, M. J. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00203 · Cited by 4 · see citing works
SCREEN: predicting single-cell gene expression perturbation responses via optimal transport
Wang, H., Wang, Y., Jiang, Q., Zhang, Y., & Chen, S. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-31014-9 · Cited by 4 · see citing works
Ex vivo tissue perturbations coupled to single cell RNA-seq reveal multi-lineage cell circuit dynamics in human lung fibrogenesisPreprint
Lang, N. J., Gote-Schniering, J., Porras-Gonzalez, D., Yang, L., Sadeleer, L. D., Jentzsch, R. C., Shitov, V. A., Zhou, S., Ansari, M., Agami, A., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.16.524219 · Cited by 4 · see citing works
Self-citation
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Modeling single-cell perturbations using deep learning
Lotfollahi, M., Theis, F., & München, T. U. (2021)
Self-citation
Deep-learning-based isolation of perturbation-induced variations in single-cell data
(2023) · Nature Methods · doi:10.1038/s41592-023-01956-2

Authors: Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J.
Affiliations: Helmholtz Munich (DE)
Corresponding author: Theis, Fabian J.
ORCID: Ji, Yuge
Lotfollahi, Mohammad
Wolf, F. Alexander
Theis, Fabian J.
Venue: Cell Systems
Publisher: Elsevier BV
Published: 2021-06
Volume: 12
Issue: 6
Pages: 522-537
ISSN: 2405-4712 (ISSN-L)
DOI: 10.1016/j.cels.2021.05.016
OpenAlex: W3170265427
Cited by: 137
References: 174
Citations by year: 2026: 37 · 2025: 29 · 2024: 28 · 2023: 26 · 2022: 14 · 2021: 3
FWCI: 1.86
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 380 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Cell Systems (publishedVersion)
PubMed (publishedVersion)
Site cant be reached (submittedVersion, other-oa)
PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
Funders: BMBF Berlin
Helmholtz Association of German Research Centres (ZT-I-007, ZT-I-PF-5-01)
Silicon Valley Community Foundation
Bundesministerium für Bildung und Forschung (01IS18036B, 01IS18053A)
Chan Zuckerberg Initiative (2019- 002438)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: single-cell omics, deep learning, cell phenotype, multi-omics integration
MeSH terms: Machine Learning, Neural Networks, Computer

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