Fetching the paper…
Reading the bibliography…
Interventions play a pivotal role in the study of complex biological systems.
Causal inference using potential outcomes: Design, modeling, decisions
Donald B Rubin · 2005
Earlier work this paper cites.
Limma: linear models for microarray data
Gordon K Smyth · 2005
Earlier work this paper cites.
Interventions and causal inference
Frederick Eberhardt and Richard Scheines · 2007
Earlier work this paper cites.
Data and theory point to mainly additive genetic variance for complex traits
William G Hill, Michael E Goddard, and Peter M Visscher · 2008
Earlier work this paper cites.
Network pharmacology: the next paradigm in drug discovery
Andrew L Hopkins · 2008
Earlier work this paper cites.
In silico studies on the sensitivity of myocardial pcr/atp to changes in mitochondrial enzyme activity and oxygen concentration
Lindsay M. Edwards, Houman Ashrafian, and Bernard Korzeniewski · 2011
Earlier work this paper cites.
Uncertainty sampling
Burr Settles · 2012
Earlier work this paper cites.
What is synergy? the saariselkä agreement revisited
Jing Tang, Krister Wennerberg, and Tero Aittokallio · 2015
Earlier work this paper cites.
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, Livnat Jerby-Arnon, Nemanja D Marjanovic, Danielle Dionne, Tyler Burks, Raktima Raychowdhury, et al · 2016
Earlier work this paper cites.
Central moment discrepancy (cmd) for domain-invariant representation learning
Werner Zellinger, Thomas Grubinger, Edwin Lughofer, Thomas Natschläger, and Susanne Saminger-Platz · 2016
Earlier work this paper cites.
Crispr/cas9 mutagenesis invalidates a putative cancer dependency targeted in on-going clinical trials
Ann Lin, Christopher J Giuliano, Nicole M Sayles, and Jason M Sheltzer · 2017
Earlier work this paper cites.
Combinatorial crispr–cas9 screens for de novo mapping of genetic interactions
John Paul Shen, Dongxin Zhao, Roman Sasik, Jens Luebeck, Amanda Birmingham, Ana Bojorquez-Gomez, Katherine Licon, Kristin Klepper, Daniel Pekin, Alex N Beckett, et al · 2017
Earlier work this paper cites.
Crispr approaches to small molecule target identification
Marco Jost and Jonathan S Weissman · 2018
Earlier work this paper cites.
Deep generative modeling for single-cell transcriptomics
Romain Lopez, Jeffrey Regier, Michael B Cole, Michael I Jordan, and Nir Yosef · 2018
Cited alongside, same era.
Using deep learning to model the hierarchical structure and function of a cell
Jianzhu Ma, Michael Ku Yu, Samson Fong, Keiichiro Ono, Eric Sage, Barry Demchak, Roded Sharan, and Trey Ideker · 2018
Cited alongside, same era.
scgen predicts single-cell perturbation responses
Mohammad Lotfollahi, F Alexander Wolf, and Fabian J Theis · 2019
Cited alongside, same era.
Exploring genetic interaction manifolds constructed from rich single-cell phenotypes
Thomas M Norman, Max A Horlbeck, Joseph M Replogle, Alex Y Ge, Albert Xu, Marco Jost, Luke A Gilbert, and Jonathan S Weissman · 2019
Cited alongside, same era.
From louvain to leiden: guaranteeing well-connected communities
Vincent A Traag, Ludo Waltman, and Nees Jan Van Eck · 2019
Cited alongside, same era.
Identifiability guarantees for causal disentanglement from soft interventions
Recover identifies synergistic drug combinations in vitro through sequential model optimization
Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma, Thomas Gaudelet, Andrew Anighoro, Torsten Gross, Francisco Martínez-Peña, Eileen L Tang, MS Suraj, Cristian Regep, et al · 2023
Later among the works it cites.
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Dhananjay Bhaskar, Sumner Magruder, Edward De Brouwer, Aarthi Venkat, Frederik Wenkel, Guy Wolf, and Smita Krishnaswamy · 2023
Later among the works it cites.
Advances in crispr therapeutics
Michael Chavez, Xinyi Chen, Paul B Finn, and Lei S Qi · 2023
Later among the works it cites.
Transcriptomic forecasting with neural ordinary differential equations
Rossin Erbe, Genevieve Stein-O’Brien, and Elana J Fertig · 2023
Later among the works it cites.
Renge infers gene regulatory networks using time-series single-cell rna-seq data with crispr perturbations
Masato Ishikawa, Seiichi Sugino, Yoshie Masuda, Yusuke Tarumoto, Yusuke Seto, Nobuko Taniyama, Fumi Wagai, Yuhei Yamauchi, Yasuhiro Kojima, Hisanori Kiryu, et al · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jiaqi Zhang, Chandler Squires, Kristjan Greenewald, Akash Srivastava, Karthikeyan Shanmugam, and Caroline Uhler · 2019
Cited alongside, same era.
Crispr screen in mechanism and target discovery for cancer immunotherapy
Dan Liu, Xuan Zhao, Anqun Tang, Xiyue Xu, Shuci Liu, Li Zha, Wen Ma, Junnian Zheng, and Ming Shi · 2020
Cited alongside, same era.
Multimodal pooled perturb-cite-seq screens in patient models define mechanisms of cancer immune evasion
Chris J Frangieh, Johannes C Melms, Pratiksha I Thakore, Kathryn R Geiger-Schuller, Patricia Ho, Adrienne M Luoma, Brian Cleary, Livnat Jerby-Arnon, Shruti Malu, Michael S Cuoco, et al · 2021
Cited alongside, same era.
bayesynergy: flexible bayesian modelling of synergistic interaction effects in in vitro drug combination experiments
Leiv Rønneberg, Andrea Cremaschi, Robert Hanes, Jorrit M Enserink, and Manuela Zucknick · 2021
Cited alongside, same era.
Priors in bayesian deep learning: A review
Vincent Fortuin · 2022
Cited alongside, same era.
Mapping information-rich genotype-phenotype landscapes with genome-scale perturb-seq
Joseph M Replogle, Reuben A Saunders, Angela N Pogson, Jeffrey A Hussmann, Alexander Lenail, Alina Guna, Lauren Mascibroda, Eric J Wagner, Karen Adelman, Gila Lithwick-Yanai, et al · 2022
Cited alongside, same era.
Pyrelational: A library for active learning research and development
Paul Scherer, Thomas Gaudelet, Alison Pouplin, Jyothish Soman, Lindsay Edwards, Jake P Taylor-King, et al · 2022
Cited alongside, same era.
Later among the works it cites.
Optimal distance metrics for single-cell rna-seq populations
Yuge Ji, Tessa Green, Stefan Peidli, Mojtaba Bahrami, Meiqi Liu, Luke Zappia, Karin Hrovatin, Chris Sander, and Fabian Theis · 2023
Later among the works it cites.
Crispr-cas system is an effective tool for identifying drug combinations that provide synergistic therapeutic potential in cancers
Yuna Kim and Hyeong-Min Lee · 2023
Later among the works it cites.
Predicting cellular responses to complex perturbations in high-throughput screens
Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Leon Hetzel, Yuge Ji, Ignacio L Ibarra, Sanjay R Srivatsan, Mohsen Naghipourfar, Riza M Daza, Beth Martin, et al · 2023
Later among the works it cites.
Synergistic effects of chemical mixtures: how frequent is rare?
Olwenn V Martin · 2023
Later among the works it cites.
Predicting transcriptional outcomes of novel multigene perturbations with gears
Yusuf Roohani, Kexin Huang, and Jure Leskovec · 2023
Later among the works it cites.
Efficient combinatorial targeting of rna transcripts in single cells with cas13 rna perturb-seq
Hans-Hermann Wessels, Alejandro Méndez-Mancilla, Yuhan Hao, Efthymia Papalexi, William M Mauck III, Lu Lu, John A Morris, Eleni P Mimitou, Peter Smibert, Neville E Sanjana, et al · 2023
Later among the works it cites.
scperturb: harmonized single-cell perturbation data
Stefan Peidli, Tessa D Green, Ciyue Shen, Torsten Gross, Joseph Min, Samuele Garda, Bo Yuan, Linus J Schumacher, Jake P Taylor-King, Debora S Marks, et al · 2024
Closest in time.