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Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.
Perturb-seq: dissecting molecular circuits with scalable single-cell rna profiling of pooled genetic screens
Atray Dixit, Oren Parnas, Biyu Li, Jenny Chen, Charles P Fulco, et al · 2016
Earlier work this paper cites.
Single cells make big data: New challenges and opportunities in transcriptomics
Philipp Angerer, Lukas Simon, Sophie Tritschler, F Alexander Wolf, David Fischer, and Fabian J Theis · 2017
Earlier work this paper cites.
Fader networks: Manipulating images by sliding attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, et al · 2017
Earlier work this paper cites.
A next generation connectivity map: L1000 platform and the first 1,000,000 profiles
Aravind Subramanian, Rajiv Narayan, Steven M Corsello, David D Peck, Ted E Natoli, et al · 2017
Earlier work this paper cites.
Efficient parameter estimation enables the prediction of drug response using a mechanistic pan-cancer pathway model
Fabian Fröhlich, Thomas Kessler, Daniel Weindl, Alexey Shadrin, Leonard Schmiester, et al · 2018
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Highly multiplexed single-cell rna-seq for defining cell population and transcriptional spaces
Jase Gehring, Jong Hwee Park, Sisi Chen, Matthew Thomson, and Lior Pachter · 2018
Earlier work this paper cites.
Which training methods for gans do actually converge?
Lars Mescheder, Andreas Geiger, and Sebastian Nowozin · 2018
Earlier work this paper cites.
Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics
Marlon Stoeckius, Shiwei Zheng, Brian Houck-Loomis, Stephanie Hao, Bertrand Z Yeung, et al · 2018
Earlier work this paper cites.
scgen predicts single-cell perturbation responses
Mohammad Lotfollahi, F Alexander Wolf, and Fabian J Theis · 2019
Earlier work this paper cites.
Multi-seq: sample multiplexing for single-cell rna sequencing using lipid-tagged indices
Christopher S McGinnis, David M Patterson, Juliane Winkler, Daniel N Conrad, Marco Y Hein, et al · 2019
Earlier work this paper cites.
Exploring genetic interaction manifolds constructed from rich single-cell phenotypes
Thomas M Norman, Max A Horlbeck, Joseph M Replogle, Alex Y Ge, Albert Xu, et al · 2019
Cited alongside, same era.
Dr. vae: improving drug response prediction via modeling of drug perturbation effects
Ladislav Rampášek, Daniel Hidru, Petr Smirnov, Benjamin Haibe-Kains, and Anna Goldenberg · 2019
Cited alongside, same era.
Construction of a human cell landscape at single-cell level
Xiaoping Han, Ziming Zhou, Lijiang Fei, Huiyu Sun, Renying Wang, et al · 2020
Cited alongside, same era.
Celloracle: Dissecting cell identity via network inference and in silico gene perturbation
Kenji Kamimoto, Christy M Hoffmann, and Samantha A Morris · 2020
Cited alongside, same era.
Enhancing scientific discoveries in molecular biology with deep generative models
Romain Lopez, Adam Gayoso, and Nir Yosef · 2020
Cited alongside, same era.
Machine learning for perturbational single-cell omics
Yuge Ji, Mohammad Lotfollahi, F Alexander Wolf, and Fabian J Theis · 2021
Later among the works it cites.
Learning interpretable cellular responses to complex perturbations in high-throughput screens
Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Yuge Ji, Ignacio L Ibarra, et al · 2021
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A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to covid-19 drug repurposing
Thai-Hoang Pham, Yue Qiu, Jucheng Zeng, Lei Xie, and Ping Zhang · 2021
Later among the works it cites.
Vega is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
Lucas Seninge, Ioannis Anastopoulos, Hongxu Ding, and Joshua Stuart · 2021
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Deepcellstate: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment
Ramzan Umarov, Yu Li, and Erik Arner · 2021
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Learning interpretable latent autoencoder representations with annotations of feature sets
Sergei Rybakov, Mohammad Lotfollahi, Fabian J Theis, and F Alexander Wolf · 2020
Cited alongside, same era.
Massively multiplex chemical transcriptomics at single-cell resolution
Sanjay R Srivatsan, José L McFaline-Figueroa, Vijay Ramani, Lauren Saunders, Junyue Cao, et al · 2020
Cited alongside, same era.
Single-cell genomic approaches for developing the next generation of immunotherapies
Ido Yofe, Rony Dahan, and Ido Amit · 2020
Cited alongside, same era.
Generating hard-to-obtain information from easy-to-obtain information: applications in drug discovery and clinical inference
Matthew Amodio, Dennis Shung, Daniel B Burkhardt, Patrick Wong, Michael Simonov, et al · 2021
Cited alongside, same era.
Graph representation learning for single-cell biology
Leon Hetzel, David S Fischer, Stephan Günnemann, and Fabian J Theis · 2021
Cited alongside, same era.
Later among the works it cites.
Cellbox: interpretable machine learning for perturbation biology with application to the design of cancer combination therapy
Bo Yuan, Ciyue Shen, Augustin Luna, Anil Korkut, Debora S Marks, et al · 2021
Later among the works it cites.
Prediction of drug efficacy from transcriptional profiles with deep learning
Jie Zhu, Jingxiang Wang, Xin Wang, Mingjing Gao, Bingbing Guo, et al · 2021
Later among the works it cites.
A python library for probabilistic analysis of single-cell omics data
Adam Gayoso, Romain Lopez, Galen Xing, Pierre Boyeau, Valeh Valiollah Pour Amiri, et al · 2022
Closest in time.
Mapping single-cell data to reference atlases by transfer learning
Mohammad Lotfollahi, Mohsen Naghipourfar, Malte D Luecken, Matin Khajavi, Maren Büttner, et al · 2022
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An integrated cell atlas of the human lung in health and disease
Lisa Sikkema, Daniel C Strobl, Luke Zappia, Elo Madissoon, Nikolay S Markov, et al · 2022
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