The DOI matches its Crossref record; 181 of the 181 references it cites were found in a registry or an index.
Machine learning for perturbational single-cell omics
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
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
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
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
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
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
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
(2008) · Journal of Machine Learning Research · Cited by 33,051
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
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
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015
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
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
(2016) · Cited by 11,679
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
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
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
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
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 6,245
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
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 4,354
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
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
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
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
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
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
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
Self-citationLearning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761
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
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
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
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
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
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
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
Self-citationHuman 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
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
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
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
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
Ji, Y.-R., Zhou, Z., Liu, H., & Davuluri, R. (2020) · bioRxiv · doi:10.1101/2020.09.17.301879 · Cited by 1,426
Self-citationCao, 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
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
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
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
Ji, Y., Zhou, Z., Liu, H., & Davuluri, R. V. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab083 · Cited by 1,303
Self-citationDeep 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 1,288
Self-citationAdamson, 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
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 1,147
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
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
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
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 1,012
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 883
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 828
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 782
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
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
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
Self-citationMULTI-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
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
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
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
Massively multiplex single-cell Hi-C
Ramani, V., Deng, X., Qiu, R., Gunderson, K. L., Steemers, F. J., Distèche, C. M., Noble, W. S., Duan, Z., & Shendure, J. (2017) · Nature Methods · doi:10.1038/nmeth.4155 · Cited by 545
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
Karimi, M., Wu, D. J., Wang, Z., & Shen, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz111 · Cited by 540
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
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 531
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
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
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 494
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
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 455
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
Méndez‐Lucio, O., Baillif, B., Clevert, D.-A., Rouquié, D., & Wichard, J. (2020) · Nature Communications · doi:10.1038/s41467-019-13807-w · Cited by 441
In Search of Lost Domain Generalization
Gulrajani, I., & Lopez-Paz, D. (2020) · arXiv · doi:10.48550/arxiv.2007.01434 · Cited by 440
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 436
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 407
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
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
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 371
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 347
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
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
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 324
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
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 297
Yang, H., Sun, L., Li, W., Liu, G., & Tang, Y. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00030 · Cited by 287
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
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 280
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
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
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
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
Way, G. P., & Greene, C. S. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0008 · Cited by 253
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
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 228
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
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
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
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
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
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
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
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
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
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 171
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
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 164
Single-cell analyses to tailor treatments
Shalek, A. K., & Benson, M. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aan4730 · Cited by 163
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
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 144
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
Self-citationThe 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 140
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 138
Fortelny, N., & Bock, C. (2020) · Genome Biology · doi:10.1186/s13059-020-02100-5 · Cited by 135
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
Self-citationAdvances 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 130
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 125
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 123
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
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 107
Shin, D., Lee, W., Lee, J. H., & Bang, D. (2019) · Science Advances · doi:10.1126/sciadv.aav2249 · Cited by 101
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 99
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
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
Zachariadis, V., Cheng, H., Andrews, N., & Enge, M. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.09.025 · Cited by 92
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
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 89
Luquette, L. J., Bohrson, C. L., Sherman, M. A., & Park, P. J. (2019) · Nature Communications · doi:10.1038/s41467-019-11857-8 · Cited by 85
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 85
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
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
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 80
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 72
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
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 65
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 64
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
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
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 56
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
PharmDock: a pharmacophore-based docking program
Hu, B., & Lill, M. A. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-14 · Cited by 55
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
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 50
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
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 48
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
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
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
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
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
Self-citationThe 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 36
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 36
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
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
Self-citationA 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 31
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 29
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
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
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
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
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
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
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
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
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
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
Causal Imputation via Synthetic InterventionsPreprint
Squires, C., Shen, D., Agarwal, A., Shah, D., & Uhler, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03127 · Cited by 11
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
Ji, Y., & Gao, S. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 10
Self-citationJiang, Y., Rensi, S., Wang, S., & Altman, R. B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.17.385757 · Cited by 9
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
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
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
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
Umarov, R., Li, Y., & Arner, E. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.14.422792 · Cited by 1
Research works printed but not linked: 4
- Burkhardt, 2019, Quantifying the effect of experimental perturbations in single-cell RNA-sequencing data using graph signal processing, bioRxiv
- Cao, 2017, Comprehensive single-cell transcriptional profiling of a multicellular organism, Science, 357, 661, 10.1126/science.aam8940 · DOI · Paper page
- Sohn, 2015, Learning structured output representation using deep conditional generative models, Adv. Neural Inf. Process. Syst., 28, 3483
- Umarov, 2020, A DeepFake framework for prediction of cell type specific transcriptional states induced by drug treatment, bioRxiv
Cited by
Sorted by citations
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 1,694
“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
“3).”
Self-citationDissecting 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
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 462
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
Metadata
- Authors
- Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Corresponding author
- Theis, Fabian J.
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 12
- Issue
- 6
- Pages
- 522-537
- ISSN
- 2405-4712 (ISSN-L)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
