A practical application of generative adversarial networks for RNA-seq analysis to predict the molecular progress of Alzheimer's disease
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pcbi.1008099
Download full text from publisher
References listed on IDEAS
- Gökcen Eraslan & Lukas M. Simon & Maria Mircea & Nikola S. Mueller & Fabian J. Theis, 2019. "Single-cell RNA-seq denoising using a deep count autoencoder," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
- Jiarui Ding & Anne Condon & Sohrab P. Shah, 2018. "Interpretable dimensionality reduction of single cell transcriptome data with deep generative models," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Minhyeok Lee, 2023. "Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review," Mathematics, MDPI, vol. 11(14), pages 1-26, July.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Andrea Riba & Attila Oravecz & Matej Durik & Sara Jiménez & Violaine Alunni & Marie Cerciat & Matthieu Jung & Céline Keime & William M. Keyes & Nacho Molina, 2022. "Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
- Xiang Lin & Tian Tian & Zhi Wei & Hakon Hakonarson, 2022. "Clustering of single-cell multi-omics data with a multimodal deep learning method," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
- Ethan Bahl & Snehajyoti Chatterjee & Utsav Mukherjee & Muhammad Elsadany & Yann Vanrobaeys & Li-Chun Lin & Miriam McDonough & Jon Resch & K. Peter Giese & Ted Abel & Jacob J. Michaelson, 2024. "Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
- Ziqi Zhang & Xinye Zhao & Mehak Bindra & Peng Qiu & Xiuwei Zhang, 2024. "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
- Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
- Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
- Songming Tang & Xuejian Cui & Rongxiang Wang & Sijie Li & Siyu Li & Xin Huang & Shengquan Chen, 2024. "scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
- Ian Covert & Rohan Gala & Tim Wang & Karel Svoboda & Uygar Sümbül & Su-In Lee, 2023. "Predictive and robust gene selection for spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
- Jing Qi & Yang Zhou & Zicen Zhao & Shuilin Jin, 2021. "SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-20, June.
- Zhijian Li & Christoph Kuppe & Susanne Ziegler & Mingbo Cheng & Nazanin Kabgani & Sylvia Menzel & Martin Zenke & Rafael Kramann & Ivan G. Costa, 2021. "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
- Zhenchao Tang & Guanxing Chen & Shouzhi Chen & Jianhua Yao & Linlin You & Calvin Yu-Chian Chen, 2024. "Modal-nexus auto-encoder for multi-modality cellular data integration and imputation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
- Xin Tang & Jiawei Zhang & Yichun He & Xinhe Zhang & Zuwan Lin & Sebastian Partarrieu & Emma Bou Hanna & Zhaolin Ren & Hao Shen & Yuhong Yang & Xiao Wang & Na Li & Jie Ding & Jia Liu, 2023. "Explainable multi-task learning for multi-modality biological data analysis," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
- George C. Linderman & Jun Zhao & Manolis Roulis & Piotr Bielecki & Richard A. Flavell & Boaz Nadler & Yuval Kluger, 2022. "Zero-preserving imputation of single-cell RNA-seq data," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
- Lucy Xia & Christy Lee & Jingyi Jessica Li, 2024. "Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
- Md Tauhidul Islam & Jen-Yeu Wang & Hongyi Ren & Xiaomeng Li & Masoud Badiei Khuzani & Shengtian Sang & Lequan Yu & Liyue Shen & Wei Zhao & Lei Xing, 2022. "Leveraging data-driven self-consistency for high-fidelity gene expression recovery," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
- Vidhya M. Ravi & Nicolas Neidert & Paulina Will & Kevin Joseph & Julian P. Maier & Jan Kückelhaus & Lea Vollmer & Jonathan M. Goeldner & Simon P. Behringer & Florian Scherer & Melanie Boerries & Marie, 2022. "T-cell dysfunction in the glioblastoma microenvironment is mediated by myeloid cells releasing interleukin-10," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Yasa Baig & Helena R. Ma & Helen Xu & Lingchong You, 2023. "Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
- Yuge Wang & Hongyu Zhao, 2022. "Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders," PLOS Computational Biology, Public Library of Science, vol. 18(4), pages 1-31, April.
- Xinyi Zhang & Xiao Wang & G. V. Shivashankar & Caroline Uhler, 2022. "Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1008099. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.