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We introduce a comprehensive framework for modeling single cell transcriptomic responses to perturbations, aimed at standardizing benchmarking in this rapidly evolving field.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E. Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019) · 1912
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958
Earlier work this paper cites.
Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. (2020) · 2001
Earlier work this paper cites.
Evaluating web-based question answering systems
Radev, D. R., Qi, H., Wu, H., and Fan, W. (2002) · 2002
Earlier work this paper cites.
mrna-seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · 2009
Earlier work this paper cites.
Matching methods for causal inference: A review and a look forward
Stuart, E. A. (2010) · 2010
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011) · 2011
Earlier work this paper cites.
Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P. (2013) · 2013
Earlier work this paper cites.
Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells
Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A., Mikkelsen, T. S., Heckl, D., Ebert, B. L., Root, D. E., Doench, J. G., and Zhang, F. (2014) · 2014
Earlier work this paper cites.
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., Trombetta, J. J., Weitz, D. A., Sanes, J. R., Shalek, A. K., Regev, A., and McCarroll, S. A. (2015) · 2015
Earlier work this paper cites.
The Chemical Space Project
Reymond, J.-L. (2015) · 2015
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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) · 2016
Earlier work this paper cites.
Ba, J. L., Kiros, J. R., and Hinton, G. E. (2016) · 2016
Earlier work this paper cites.
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) · 2016
Earlier work this paper cites.
Transcriptomics technologies
Lowe, R., Shirley, N., Bleackley, M., Dolan, S., and Shafee, T. (2017) · 2017
Earlier work this paper cites.
Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition
Jaeger, S., Fulle, S., and Turk, S. (2018) · 2018
Earlier work this paper cites.
Scanpy: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., and Theis, F. J. (2018) · 2018
Earlier work this paper cites.
Optuna: A next-generation hyperparameter optimization framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019) · 2019
Earlier work this paper cites.
Class-balanced loss based on effective number of samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S. (2019) · 2019
Earlier work this paper cites.
PyTorch Lightning
Falcon, W. and The PyTorch Lightning team (2019) · 2019
Cited alongside, same era.
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., and Weissman, J. S. (2019) · 2019
Cited alongside, same era.
Hydra - a framework for elegantly configuring complex applications
Yadan, O. (2019) · 2019
Cited alongside, same era.
Computational approaches for effective CRISPR guide RNA design and evaluation
Liu, G., Zhang, Y., and Zhang, T. (2020) · 2020
Cited alongside, same era.
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., Zhang, F., Steemers, F., Shendure, J., and Trapnell, C. (2020) · 2020
Cited alongside, same era.
Applications of single-cell RNA sequencing in drug discovery and development
Van de Sande, B., Lee, J. S., Mutasa-Gottgens, E., Naughton, B., Bacon, W., Manning, J., Wang, Y., Pollard, J., Mendez, M., Hill, J., Kumar, N., Cao, X., Chen, X., Khaladkar, M., Wen, J., Leach, A., and Ferran, E. (2023) · 2023
Later among the works it cites.
Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear methods
Ahlmann-Eltze, C., Huber, W., and Anders, S. (2024) · 2024
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Modelling cellular perturbations with the sparse additive mechanism shift variational autoencoder
Bereket, M. and Karaletsos, T. (2024) · 2024
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scGPT: toward building a foundation model for single-cell multi-omics using generative AI
Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., Duan, N., and Wang, B. (2024) · 2024
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Reflections on ICLR24 (or ‘Why is AI for drug discovery so difficult to get right?’)
Edwards, L. (2024) · 2024
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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., Zhao, M., Ager, C. R., Rogava, M., Hovey, L., Rotem, A., Bernatchez, C., Wucherpfennig, K. W., Johnson, B. E., Rozenblatt-Rosen, O., Schadendorf, D., Regev, A., and Izar, B. (2021) · 2021
Cited alongside, same era.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., and Fergus, R. (2021) · 2021
Cited alongside, same era.
High-content CRISPR screening
Bock, C., Datlinger, P., Chardon, F., Coelho, M. A., Dong, M. B., Lawson, K. A., Lu, T., Maroc, L., Norman, T. M., Song, B., Stanley, G., Chen, S., Garnett, M., Li, W., Moffat, J., Qi, L. S., Shapiro, R. S., Shendure, J., Weissman, J. S., and Zhuang, X. (2022) · 2022
Cited alongside, same era.
Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ica
Lachapelle, S., Rodriguez, P., Sharma, Y., Everett, K. E., Le Priol, R., Lacoste, A., and Lacoste-Julien, S. (2022) · 2022
Cited alongside, same era.
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
Lopez, R., Tagasovska, N., Ra, S., Cho, K., Pritchard, J. K., and Regev, A. (2022) · 2022
Cited alongside, same era.
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., Iremadze, N., Oberstrass, F., Lipson, D., Bonnar, J. L., Jost, M., Norman, T. M., and Weissman, J. S. (2022) · 2022
Cited alongside, same era.
Large-scale chemical language representations capture molecular structure and properties
Ross, J., Belgodere, B., Chenthamarakshan, V., Padhi, I., Mroueh, Y., and Das, P. (2022) · 2022
Cited alongside, same era.
A mini-review on perturbation modelling across single-cell omic modalities
Gavriilidis, G. I., Vasileiou, V., Orfanou, A., Ishaque, N., and Psomopoulos, F. (2024) · 2024
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Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens
Jiang, L., Dalgarno, C., Papalexi, E., Mascio, I., Wessels, H.-H., Yun, H., Iremadze, N., Lithwick-Yanai, G., Lipson, D., and Satija, R. (2024a) · 2024
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Multiplex single-cell chemical genomics reveals the kinase dependence of the response to targeted therapy
McFaline-Figueroa, J. L., Srivatsan, S., Hill, A. J., Gasperini, M., Jackson, D. L., Saunders, L., Domcke, S., Regalado, S. G., Lazarchuck, P., Alvarez, S., et al. (2024) · 2024
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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., Luna, A., Blüthgen, N., and Sander, C. (2024) · 2024
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Disentanglement of single-cell data with biolord
Piran, Z., Cohen, N., Hoshen, Y., and Nitzan, M. (2024) · 2024
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A benchmark for prediction of transcriptomic responses to chemical perturbations across cell types
Szałata, A., Benz, A., Cannoodt, R., Cortes, M., Fong, J., Kuppasani, S., Lieberman, R., Liu, T., Mas-Rosario, J. A., Meinl, R., et al. (2024) · 2024
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PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction
Wenteler, A., Occhetta, M., Branson, N., Huebner, M., Curean, V., Dee, W. T., Connell, W. T., Hawkins-Hooker, A., Chung, S. P., Ektefaie, Y., Gallagher-Syed, A., and Córdova, C. M. V. (2024) · 2024
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Predicting cellular responses to perturbation across diverse contexts with state
Adduri, A. K., Gautam, D., Bevilacqua, B., Imran, A., Shah, R., Naghipourfar, M., Teyssier, N., Ilango, R., Nagaraj, S., Dong, M., Ricci-Tam, C., Carpenter, C., Subramanyam, V., Winters, A., Tirukkovular, S., Sullivan, J., Plosky, B. S., Eraslan, B., Youngblut, N. D., Leskovec, J., Gilbert, L. A., Konermann, S., Hsu, P. D., Dobin, A., Burke, D. P., Goodarzi, H., and Roohani, Y. H. (2025) · 2025
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Benchmarking foundation cell models for post-perturbation rna-seq prediction
Csendes, G., Sanz, G., Szalay, K. Z., and Szalai, B. (2025) · 2025
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Cellflow enables generative single-cell phenotype modeling with flow matching
Klein, D., Fleck, J. S., Bobrovskiy, D., Zimmermann, L., Becker, S., Palma, A., Dony, L., Tejada-Lapuerta, A., Huguet, G., Lin, H.-C., Azbukina, N., Sanchís-Calleja, F., Uscidda, T., Szalata, A., Gander, M., Regev, A., Treutlein, B., Camp, J. G., and Theis, F. J. (2025) · 2025
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Virtual cell challenge: Toward a turing test for the virtual cell
Roohani, Y. H., Hua, T. J., Tung, P.-Y., Bounds, L. R., Yu, F. B., Dobin, A., Teyssier, N., Adduri, A., Woodrow, A., Plosky, B. S., Mehta, R., Hsu, B., Sullivan, J., Ricci-Tam, C., Li, N., Kazaks, J., Gilbert, L. A., Konermann, S., Hsu, P. D., Goodarzi, H., and Burke, D. P. (2025) · 2025
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Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations
Wong, D. R., Hill, A. S., and Moccia, R. (2025) · 2025
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Tahoe-100m: A giga-scale single-cell perturbation atlas for context-dependent gene function and cellular modeling
Zhang, J., Ubas, A. A., de Borja, R., Svensson, V., Thomas, N., Thakar, N., Winters, A., Khan, U., Jones, M. G., Thompson, J. D., Tran, V., Pangallo, J., Papalexi, E., Sapre, A., Nguyen, H., Sanderson, O., Nigos, M., Kaplan, O., Schroeder, S., Hariadi, B., Marrujo, S., Salvino, C. C. A., Gallareta Olivares, G., Koehler, R., Geiss, G., Rosenberg, A., Roco, C., Merico, D., Alidoust, N., Goodarzi, H., and Yu, J. (2025) · 2025
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