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Recent technological advances have introduced new high-throughput methods for studying host-virus interactions, but testing synergistic interactions between host gene pairs during infection remains relatively slow and labor intensive.
Robust estimation of a location parameter
Peter J Huber · 1992
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Optimal design of experiments
Friedrich Pukelsheim · 2006
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Hiv-1 antiretroviral resistance: scientific principles and clinical applications
Michele W Tang and Robert W Shafer · 2012
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2014
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Hiv tat controls rna polymerase ii and the epigenetic landscape to transcriptionally reprogram target immune cells
Jonathan E Reeder, Youn-Tae Kwak, Ryan P McNamara, Christian V Forst, and Iván D’Orso · 2015
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Antiviral therapy
Douglas D Richman and Neal Nathanson · 2016
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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A crispr toolbox to study virus–host interactions
Andreas S Puschnik, Karim Majzoub, Yaw Shin Ooi, and Jan E Carette · 2017
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A review of active learning approaches to experimental design for uncovering biological networks
Yuriy Sverchkov and Mark Craven · 2017
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Goatools: A python library for gene ontology analyses
Drew V Klopfenstein, Luke Zhang, Brent S Pedersen, et al · 2018
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2019
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Bilinear bandits with low-rank structure
Kwang-Sung Jun, Rebecca Willett, Stephen Wright, and Robert Nowak · 2019
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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
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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
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Discobax discovery of optimal intervention sets in genomic experiment design
Clare Lyle, Arash Mehrjou, Pascal Notin, Andrew Jesson, Stefan Bauer, Yarin Gal, and Patrick Schwab · 2023
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The scalable precision medicine open knowledge engine (spoke): a massive knowledge graph of biomedical information
John H Morris, Karthik Soman, Rabia E Akbas, Xiaoyuan Zhou, Brett Smith, Elaine C Meng, Conrad C Huang, Gabriel Cerono, Gundolf Schenk, Angela Rizk-Jackson, et al · 2023
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A survey on oversmoothing in graph neural networks
T Konstantin Rusch, Michael M Bronstein, and Siddhartha Mishra · 2023
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A geometric analysis of neural collapse with unconstrained features
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A quantitative genetic interaction map of hiv infection
David E Gordon, Ariane Watson, Assen Roguev, Simin Zheng, Gwendolyn M Jang, Joshua Kane, Jiewei Xu, Jeffrey Z Guo, Erica Stevenson, Danielle L Swaney, et al · 2020
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Towards deeper graph neural networks with differentiable group normalization
Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu · 2020
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, et al · 2021
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Genedisco: A benchmark for experimental design in drug discovery
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, and Patrick Schwab · 2021
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Best arm identification in graphical bilinear bandits
Geovani Rizk, Albert Thomas, Igor Colin, Rida Laraki, and Yann Chevaleyre · 2021
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Neural collapse with unconstrained features
Dustin G Mixon, Hans Parshall, and Jianzong Pi · 2022
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Neural design for genetic perturbation experiments
Aldo Pacchiano, Drausin Wulsin, Robert A Barton, and Luis Voloch
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Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, and Qing Qu · 2023
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Crispr-gpt: An llm agent for automated design of gene-editing experiments
Kaixuan Huang, Yuanhao Qu, Henry Cousins, William A Johnson, Di Yin, Mihir Shah, Denny Zhou, Russ Altman, Mengdi Wang, and Le Cong · 2024
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Sequential optimal experimental design of perturbation screens guided by multi-modal priors
Kexin Huang, Romain Lopez, Jan-Christian Hütter, Takamasa Kudo, Antonio Rios, and Aviv Regev · 2024
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Automated discovery of pairwise interactions from unstructured data
Moksh Jain, Ali Denton, Shawn Whitfield, Aniket Didolkar, Berton Earnshaw, Jason Hartford, et al · 2024
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Active learning to discover pairwise genetic interactions via representation learning
Moksh Jain, Alisandra Kaye Denton, Shawn T Whitfield, Aniket Rajiv Didolkar, Berton Earnshaw, and Jason Hartford · 2024
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Biodiscoveryagent: An ai agent for designing genetic perturbation experiments
Yusuf Roohani, Andrew Lee, Qian Huang, Jian Vora, Zachary Steinhart, Kexin Huang, Alexander Marson, Percy Liang, and Jure Leskovec · 2024
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Hallucination is inevitable: An innate limitation of large language models
Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli · 2024
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