Fetching the paper…
Reading the bibliography…
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
Probabilistic reasoning in intelligent systems: networks of plausible inference
Judea Pearl · 1988
Earlier work this paper cites.
Guilt-by-association goes global
Stephen Oliver · 2000
Earlier work this paper cites.
Regulation of c-myc expression by ifn- γ \gamma through stat1-dependent and-independent pathways
Chilakamarti V Ramana, Nicholas Grammatikakis, Mikhail Chernov, Hannah Nguyen, Kee Chuan Goh, Bryan RG Williams, and George R Stark · 2000
Earlier work this paper cites.
Asymptotic statistics , volume 3
Aad W Van der Vaart · 2000
Earlier work this paper cites.
Specificity of gene regulation
Beverly M Emerson · 2002
Earlier work this paper cites.
Targeted maximum likelihood learning
Mark J Van Der Laan and Daniel Rubin · 2006
Earlier work this paper cites.
The regulatory genome: gene regulatory networks in development and evolution
Eric H Davidson · 2010
Earlier work this paper cites.
Chea: transcription factor regulation inferred from integrating genome-wide chip-x experiments
Alexander Lachmann, Huilei Xu, Jayanth Krishnan, Seth I Berger, Amin R Mazloom, and Avi Ma’ayan · 2010
Earlier work this paper cites.
Integrated genome-wide analysis of transcription factor occupancy, rna polymerase ii binding and steady-state rna levels identify differentially regulated functional gene classes
Michal Mokry, Pantelis Hatzis, Jurian Schuijers, Nico Lansu, Frans-Paul Ruzius, Hans Clevers, and Edwin Cuppen · 2012
Earlier work this paper cites.
Comparative studies of gene expression and the evolution of gene regulation
Irene Gallego Romero, Ilya Ruvinsky, and Yoav Gilad · 2012
Earlier work this paper cites.
Transcription factors: from enhancer binding to developmental control
François Spitz and Eileen EM Furlong · 2012
Earlier work this paper cites.
Understanding gene regulatory mechanisms by integrating chip-seq and rna-seq data: statistical solutions to biological problems
Claudia Angelini and Valerio Costa · 2014
Earlier work this paper cites.
The characteristic direction: a geometrical approach to identify differentially expressed genes
Neil R Clark, Kevin S Hu, Axel S Feldmann, Yan Kou, Edward Y Chen, Qiaonan Duan, and Avi Ma’ayan · 2014
Earlier work this paper cites.
Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks
Frank Emmert-Streib, Matthias Dehmer, and Benjamin Haibe-Kains · 2014
Cited alongside, same era.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Cited alongside, same era.
Supervised, semi-supervised and unsupervised inference of gene regulatory networks
Stefan R Maetschke, Piyush B Madhamshettiwar, Melissa J Davis, and Mark A Ragan · 2014
Cited alongside, same era.
Principle angle enrichment analysis (paea): Dimensionally reduced multivariate gene set enrichment analysis tool
Neil R Clark, Maciej Szymkiewicz, Zichen Wang, Caroline D Monteiro, Matthew R Jones, and Avi Ma’ayan · 2015
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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
Later among the works it cites.
Celloracle: Dissecting cell identity via network inference and in silico gene perturbation
Kenji Kamimoto, Christy M Hoffmann, and Samantha A Morris · 2020
Later among the works it cites.
Conditional out-of-distribution generation for unpaired data using transfer vae
Mohammad Lotfollahi, Mohsen Naghipourfar, Fabian J Theis, and F Alexander Wolf · 2020
Later among the works it cites.
Style transfer with variational autoencoders is a promising approach to rna-seq data harmonization and analysis
Nikolai Russkikh, Denis Antonets, Dmitry Shtokalo, Alexander Makarov, Yuri Vyatkin, Alexey Zakharov, and Evgeny Terentyev · 2020
Later among the works it cites.
Massively multiplex chemical transcriptomics at single-cell resolution
Sanjay R Srivatsan, José L McFaline-Figueroa, Vijay Ramani, Lauren Saunders, Junyue Cao, Jonathan Packer, Hannah A Pliner, Dana L Jackson, Riza M Daza, Lena Christiansen, et al · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Zemel, and Max Welling · 2017
Cited alongside, same era.
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, Xiaodong Lu, Joshua Gould, John F Davis, Andrew A Tubelli, Jacob K Asiedu, et al · 2017
Cited alongside, same era.
Integrating chip-seq with other functional genomics data
Shan Jiang and Ali Mortazavi · 2018
Cited alongside, same era.
Genome-wide crispr screens in primary human t cells reveal key regulators of immune function
Eric Shifrut, Julia Carnevale, Victoria Tobin, Theodore L Roth, Jonathan M Woo, Christina T Bui, P Jonathan Li, Morgan E Diolaiti, Alan Ashworth, and Alexander Marson · 2018
Cited alongside, same era.
Ganite: Estimation of individualized treatment effects using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar · 2018
Cited alongside, same era.
Graph neural networks for predicting protein functions
Vassilis N Ioannidis, Antonio G Marques, and Georgios B Giannakis · 2019
Cited alongside, same era.
Tutorial: Deriving the efficient influence curve for large models
Jonathan Levy · 2019
Cited alongside, same era.
Later among the works it cites.
Fast and flexible protein design using deep graph neural networks
Alexey Strokach, David Becerra, Carles Corbi-Verge, Albert Perez-Riba, and Philip M Kim · 2020
Later among the works it cites.
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.
scgnn is a novel graph neural network framework for single-cell rna-seq analyses
Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Ren Qi, Cankun Wang, Hongjun Fu, Qin Ma, and Dong Xu · 2021
Later among the works it cites.
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
Zhi-Jie Cao and Ge Gao · 2022
Closest in time.
Graph-based molecular representation learning
Zhichun Guo, Bozhao Nan, Yijun Tian, Olaf Wiest, Chuxu Zhang, and Nitesh V Chawla · 2022
Closest in time.
Gears: Predicting transcriptional outcomes of novel multi-gene perturbations
Yusuf Roohani, Kexin Huang, and Jure Leskovec · 2022
Closest in time.
Crispr activation and interference screens decode stimulation responses in primary human t cells
Ralf Schmidt, Zachary Steinhart, Madeline Layeghi, Jacob W Freimer, Raymund Bueno, Vinh Q Nguyen, Franziska Blaeschke, Chun Jimmie Ye, and Alexander Marson · 2022
Closest in time.
Lm-gvp: an extensible sequence and structure informed deep learning framework for protein property prediction
Zichen Wang, Steven A Combs, Ryan Brand, Miguel Romero Calvo, Panpan Xu, George Price, Nataliya Golovach, Emmanuel O Salawu, Colby J Wise, Sri Priya Ponnapalli, et al · 2022
Closest in time.