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Spatially resolved transcriptomics represents a significant advancement in single-cell analysis by offering both gene expression data and their corresponding physical locations.
Single-cell chromatin accessibility reveals principles of regulatory variation
Jason D Buenrostro, Beijing Wu, Ulrike M Litzenburger, Dave Ruff, Michael L Gonzales, Michael P Snyder, Howard Y Chang, and William J Greenleaf · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Saver: gene expression recovery for single-cell rna sequencing
Mo Huang, Jingshu Wang, Eduardo Torre, Hannah Dueck, Sydney Shaffer, Roberto Bonasio, John I Murray, Arjun Raj, Mingyao Li, and Nancy R Zhang · 2018
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An accurate and robust imputation method scimpute for single-cell rna-seq data
Wei Vivian Li and Jingyi Jessica Li · 2018
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Multivariate time series imputation with generative adversarial networks
Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, et al · 2018
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Autoimpute: Autoencoder based imputation of single-cell rna-seq data
Divyanshu Talwar, Aanchal Mongia, Debarka Sengupta, and Angshul Majumdar · 2018
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Gain: Missing data imputation using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela Schaar · 2018
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Spatial transcriptomics coming of age
Darren J Burgess · 2019
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Single-cell rna-seq denoising using a deep count autoencoder
Gökcen Eraslan, Lukas M Simon, Maria Mircea, Nikola S Mueller, and Fabian J Theis · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael I Jordan, and Nir Yosef · 2019
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Spage: spatial gene enhancement using scrna-seq
Tamim Abdelaal, Soufiane Mourragui, Ahmed Mahfouz, and Marcel JT Reinders · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Gp-vae: Deep probabilistic time series imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, and Stephan Mandt · 2020
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Gp-vae: Deep probabilistic time series imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, and Stephan Mandt · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
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Genomic data imputation with variational auto-encoders
Yeping Lina Qiu, Hong Zheng, and Olivier Gevaert · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Noise2same: Optimizing a self-supervised bound for image denoising
Yaochen Xie, Zhengyang Wang, and Shuiwang Ji · 2020
Cited alongside, same era.
Graphmae: Self-supervised masked graph autoencoders
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang · 2022
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Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution
Bin Li, Wen Zhang, Chuang Guo, Hao Xu, Longfei Li, Minghao Fang, Yinlei Hu, Xinye Zhang, Xinfeng Yao, Meifang Tang, et al · 2022
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Museum of spatial transcriptomics
Lambda Moses and Lior Pachter · 2022
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Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series
Sindhu Tipirneni and Chandan K Reddy · 2022
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Graphcpg: imputation of single-cell methylomes based on locus-aware neighboring subgraphs
Yuzhong Deng, Jianxiong Tang, Jiyang Zhang, Jianxiao Zou, Que Zhu, and Shicai Fan · 2023
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Tommaso Biancalani, Gabriele Scalia, Lorenzo Buffoni, Raghav Avasthi, Ziqing Lu, Aman Sanger, Neriman Tokcan, Charles R Vanderburg, Åsa Segerstolpe, Meng Zhang, et al · 2021
Cited alongside, same era.
Neighbor2neighbor: Self-supervised denoising from single noisy images
Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
Imputing single-cell rna-seq data by combining graph convolution and autoencoder neural networks
Jiahua Rao, Xiang Zhou, Yutong Lu, Huiying Zhao, and Yuedong Yang · 2021
Cited alongside, same era.
stplus: a reference-based method for the accurate enhancement of spatial transcriptomics
Chen Shengquan, Zhang Boheng, Chen Xiaoyang, Zhang Xuegong, and Jiang Rui · 2021
Cited alongside, same era.
Csdi: Conditional score-based diffusion models for probabilistic time series imputation
Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Computational approaches and challenges in spatial transcriptomics
Shuangsang Fang, Bichao Chen, Yong Zhang, Haixi Sun, Longqi Liu, Shiping Liu, Yuxiang Li, and Xun Xu · 2023
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A genetic algorithm for multivariate missing data imputation
Juan Carlos Figueroa-García, Roman Neruda, and German Hernandez-Pérez · 2023
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Handling missing data via max-entropy regularized graph autoencoder
Ziqi Gao, Yifan Niu, Jiashun Cheng, Jianheng Tang, Lanqing Li, Tingyang Xu, Peilin Zhao, Fugee Tsung, and Jia Li · 2023
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Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data
Xiangjie Kong, Wenfeng Zhou, Guojiang Shen, Wenyi Zhang, Nali Liu, and Yao Yang · 2023
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Deep generative modeling and clustering of single cell hi-c data
Qiao Liu, Wanwen Zeng, Wei Zhang, Sicheng Wang, Hongyang Chen, Rui Jiang, Mu Zhou, and Shaoting Zhang · 2023
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Imputation of ancient human genomes
Bárbara Sousa da Mota, Simone Rubinacci, Diana Ivette Cruz Dávalos, Carlos Eduardo G. Amorim, Martin Sikora, Niels N Johannsen, Marzena H Szmyt, Piotr Włodarczak, Anita Szczepanek, Marcin M Przybyła, et al · 2023
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Deep learning versus conventional methods for missing data imputation: A review and comparative study
Yige Sun, Jing Li, Yifan Xu, Tingting Zhang, and Xiaofeng Wang · 2023
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Egg-gae: scalable graph neural networks for tabular data imputation
Lev Telyatnikov and Simone Scardapane · 2023
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Single cells are spatial tokens: Transformers for spatial transcriptomic data imputation
Hongzhi Wen, Wenzhuo Tang, Wei Jin, Jiayuan Ding, Renming Liu, Feng Shi, Yuying Xie, and Jiliang Tang · 2023
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Discrete representation learning for modeling imaging-based spatial transcriptomics data
Dig Vijay Kumar Yarlagadda, Joan Massagué, and Christina Leslie · 2023
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