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Advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions at the single-cell level.
Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes
Åsa Segerstolpe, Athanasia Palasantza, Pernilla Eliasson, Eva-Marie Andersson, Anne-Christine Andréasson, Xiaoyan Sun, Simone Picelli, Alan Sabirsh, Maryam Clausen, Magnus K Bjursell, David M Smith, Maria Kasper, Carina Ämmälä, and Rickard Sandberg · 2016
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.
Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
Earlier work this paper cites.
Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes
Åsa Segerstolpe, Athanasia Palasantza, Pernilla Eliasson, Eva-Marie Andersson, Anne-Christine Andréasson, Xiaoyan Sun, Simone Picelli, Alan Sabirsh, Maryam Clausen, Magnus K Bjursell, David M Smith, Maria Kasper, Carina Ämmälä, and Rickard Sandberg · 2016
Earlier work this paper cites.
Massively parallel digital transcriptional profiling of single cells
Grace XY Zheng, Jessica M Terry, Phillip Belgrader, Paul Ryvkin, Zachary W Bent, Ryan Wilson, Solongo B Ziraldo, Tobias D Wheeler, Geoff P McDermott, Junjie Zhu, et al · 2017
Earlier work this paper cites.
Combination therapy in combating cancer
Reza Bayat Mokhtari, Tina S Homayouni, Narges Baluch, Evgeniya Morgatskaya, Sushil Kumar, Bikul Das, and Herman Yeger · 2017
Earlier work this paper cites.
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Kristina Preuer, Richard P I Lewis, Sepp Hochreiter, Andreas Bender, Krishna C Bulusu, and Günter Klambauer · 2017
Earlier work this paper cites.
Massively parallel digital transcriptional profiling of single cells
Grace XY Zheng, Jessica M Terry, Phillip Belgrader, Paul Ryvkin, Zachary W Bent, Ryan Wilson, Solongo B Ziraldo, Tobias D Wheeler, Geoff P McDermott, Junjie Zhu, et al · 2017
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
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Scanpy: large-scale single-cell gene expression data analysis
F Alexander Wolf, Philipp Angerer, and Fabian J Theis · 2018
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Scanpy: large-scale single-cell gene expression data analysis
F Alexander Wolf, Philipp Angerer, and Fabian J Theis · 2018
Earlier work this paper cites.
Single-cell rna-seq technologies and related computational data analysis
Geng Chen, Baitang Ning, and Tieliu Shi · 2019
Earlier work this paper cites.
Data denoising with transfer learning in single-cell transcriptomics
Jingshu Wang, Divyansh Agarwal, Mo Huang, Gang Hu, Zilu Zhou, Chengzhong Ye, and Nancy R Zhang · 2019
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Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
Earlier work this paper cites.
Singlecellnet: A computational tool to classify single cell rna-seq data across platforms and across species
Patrick Cahan Yuqi Tan · 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, Marco Jost, Luke A. Gilbert, and Jonathan S. Weissman · 2019
Earlier work this paper cites.
scVAE: variational auto-encoders for single-cell gene expression data
Christopher Heje Grønbech, Maximillian Fornitz Vording, Pascal N Timshel, Casper Kaae Sønderby, Tune H Pers, and Ole Winther · 2020
Cited alongside, same era.
Realistic in silico generation and augmentation of single-cell rna-seq data using generative adversarial networks
Mohamed Marouf, Pierre Machart, Vikas Bansal, Christoph Kilian, Daniel S. Magruder, and Stefan Bonn Christian F. Krebs · 2020
Cited alongside, same era.
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang · 2020
Cited alongside, same era.
Systematic comparison of single-cell and single-nucleus rna-sequencing methods
Jiarui Ding, Xian Adiconis, Sean K. Simmons, Monika S. Kowalczyk, Cynthia C. Hession, Nemanja D. Marjanovic, Travis K. Hughes, Marc H. Wadsworth, Tyler Burks, Lan T. Nguyen, John Y. H. Kwon, Boaz Barak, William Ge, Amanda J. Kedaigle, Shaina Carroll, Shuqiang Li, Nir Hacohen, Orit Rozenblatt-Rosen, Alex K. Shalek, Alexandra-Chloé Villani, Aviv Regev, and Joshua Z. Levin · 2020
Cited alongside, same era.
Interpretable rna foundation model from unannotated data for highly accurate rna structure and function predictions
Jiayang Chen, Zhihang Hu, Siqi Sun, Qingxiong Tan, Yixuan Wang, Qinze Yu, Licheng Zong, Liang Hong, Jin Xiao, Tao Shen, et al · 2022
Later among the works it cites.
deepSimDEF: deep neural embeddings of gene products and gene ontology terms for functional analysis of genes
Ahmad Pesaranghader, Stan Matwin, Marina Sokolova, Jean-Christophe Grenier, Robert G Beiko, and Julie Hussin · 2022
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Single-cell rna sequencing technologies and applications: A brief overview
Dragomirka Jovic, Xue Liang, Hua Zeng, Lin Lin, Fengping Xu, and Yonglun Luo · 2022
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Disco: a database of deeply integrated human single-cell omics data
Mengwei Li, Xiaomeng Zhang, Kok Siong Ang, Jingjing Ling, Raman Sethi, Nicole Yee Shin Lee, Florent Ginhoux, and Jinmiao Chen · 2022
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Deep learning tackles single-cell analysis—a survey of deep learning for scrna-seq analysis
Mario Flores, Zhentao Liu, Tinghe Zhang, Md Musaddaqui Hasib, Yu-Chiao Chiu, Zhenqing Ye, Karla Paniagua, Sumin Jo, Jianqiu Zhang, Shou-Jiang Gao, et al · 2022
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Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Actinn: automated identification of cell types in single cell rna sequencing
Feiyang Ma and Matteo Pellegrini · 2020
Cited alongside, same era.
Zero: Memory optimizations toward training trillion parameter models
Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He · 2020
Cited alongside, same era.
Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma
Andrew L. Ji, Adam J. Rubin, Kim Thrane, Sizun Jiang, David L. Reynolds, Robin M. Meyers, Margaret G. Guo, Benson M. George, Annelie Mollbrink, Joseph Bergenstråhle, Ludvig Larsson, Yunhao Bai, Bokai Zhu, Aparna Bhaduri, Jordan M. Meyers, Xavier Rovira-Clavé, S. Tyler Hollmig, Sumaira Z. Aasi, Garry P. Nolan, Joakim Lundeberg, and Paul A. Khavari · 2020
Cited alongside, same era.
Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
Cited alongside, same era.
Modeling protein using large-scale pretrain language model
Yijia Xiao, Jiezhong Qiu, Ziang Li, Chang-Yu Hsieh, and Jie Tang · 2021
Cited alongside, same era.
Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, and Jun Zhu · 2021
Cited alongside, same era.
Later among the works it cites.
scbert as a large-scale pretrained deep language model for cell type annotation of single-cell rna-seq data
Fan Yang, Wenchuan Wang, Fang Wang, Yuan Fang, Duyu Tang, Junzhou Huang, Hui Lu, and Jianhua Yao · 2022
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Statistics or biology: the zero-inflation controversy about scrna-seq data
Ruochen Jiang, Tianyi Sun, Dongyuan Song, and Jingyi Jessica Li · 2022
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On embeddings for numerical features in tabular deep learning
Yury Gorishniy, Ivan Rubachev, and Artem Babenko · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Cross-tissue immune cell analysis reveals tissue-specific features in humans
C. Domínguez Conde, C. Xu, L. B. Jarvis, D. B. Rainbow, S. B. Wells, T. Gomes, S. K. Howlett, O. Suchanek, K. Polanski, H. W. King, L. Mamanova, N. Huang, P. A. Szabo, L. Richardson, L. Bolt, E. S. Fasouli, K. T. Mahbubani, M. Prete, L. Tuck, N. Richoz, Z. K. Tuong, L. Campos, H. S. Mousa, E. J. Needham, S. Pritchard, T. Li, R. Elmentaite, J. Park, E. Rahmani, D. Chen, D. K. Menon, O. A. Bayraktar, L. K. James, K. B. Meyer, N. Yosef, M. R. Clatworthy, P. A. Sims, D. L. Farber, K. Saeb-Parsy, J. L. Jones, and S. A. Teichmann · 2022
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Gears: Predicting transcriptional outcomes of novel multi-gene perturbations
Yusuf Roohani, Kexin Huang, and Jure Leskovec · 2022
Later among the works it cites.
Deepdds: deep graph neural network with attention mechanism to predict synergistic drug combinations
Jinxian Wang, Xuejun Liu, Siyuan Shen, Lei Deng, and Hui Liu · 2022
Later among the works it cites.
Gears: Predicting transcriptional outcomes of novel multi-gene perturbations
Yusuf Roohani, Kexin Huang, and Jure Leskovec · 2022
Later among the works it cites.
Deepdds: deep graph neural network with attention mechanism to predict synergistic drug combinations
Jinxian Wang, Xuejun Liu, Siyuan Shen, Lei Deng, and Hui Liu · 2022
Later among the works it cites.
Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al · 2023
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