Comparative optimization of polysaccharide-based nanoformulations for cardiac RNAi therapy
Author
Abstract
Suggested Citation
DOI: 10.1038/s41467-024-49804-x
Download full text from publisher
References listed on IDEAS
- Yinghua Zhao & Xiao Chu & Jintong Chen & Ying Wang & Sujun Gao & Yuxue Jiang & Xiaoqing Zhu & Guangyun Tan & Wenjie Zhao & Huanfa Yi & Honglin Xu & Xingzhe Ma & Yong Lu & Qing Yi & Siqing Wang, 2016. "Dectin-1-activated dendritic cells trigger potent antitumour immunity through the induction of Th9 cells," Nature Communications, Nature, vol. 7(1), pages 1-12, November.
- Gordon D. Brown & Siamon Gordon, 2001. "A new receptor for β-glucans," Nature, Nature, vol. 413(6851), pages 36-37, September.
- Helen S. Goodridge & Christopher N. Reyes & Courtney A. Becker & Tamiko R. Katsumoto & Jun Ma & Andrea J. Wolf & Nandita Bose & Anissa S. H. Chan & Andrew S. Magee & Michael E. Danielson & Arthur Weis, 2011. "Activation of the innate immune receptor Dectin-1 upon formation of a ‘phagocytic synapse’," Nature, Nature, vol. 472(7344), pages 471-475, April.
- Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
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.- Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
- Sauvenier, Mathieu & Van Bellegem, Sébastien, 2023. "Direction Identification and Minimax Estimation by Generalized Eigenvalue Problem in High Dimensional Sparse Regression," LIDAM Discussion Papers CORE 2023005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Ahmed Ismaïl & Hartikainen Anna-Liisa & Järvelin Marjo-Riitta & Richardson Sylvia, 2011. "False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-20, November.
- Shan Luo & Zehua Chen, 2014. "Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1229-1240, September.
- Shi Chen & Wolfgang Karl Hardle & Brenda L'opez Cabrera, 2020. "Regularization Approach for Network Modeling of German Power Derivative Market," Papers 2009.09739, arXiv.org.
- Wang, Christina Dan & Chen, Zhao & Lian, Yimin & Chen, Min, 2022. "Asset selection based on high frequency Sharpe ratio," Journal of Econometrics, Elsevier, vol. 227(1), pages 168-188.
- Laurent Ferrara & Anna Simoni, 2023.
"When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
- Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers 2019-04, Center for Research in Economics and Statistics.
- Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Post-Print hal-03919944, HAL.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," EconomiX Working Papers 2020-11, University of Paris Nanterre, EconomiX.
- Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working papers 717, Banque de France.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Papers 2007.00273, arXiv.org, revised Sep 2022.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers hal-04159714, HAL.
- Caroline Jardet & Baptiste Meunier, 2022.
"Nowcasting world GDP growth with high‐frequency data,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
- Jardet Caroline & Meunier Baptiste, 2020. "Nowcasting World GDP Growth with High-Frequency Data," Working papers 788, Banque de France.
- Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Post-Print hal-03647097, HAL.
- Peter Bühlmann & Jacopo Mandozzi, 2014. "High-dimensional variable screening and bias in subsequent inference, with an empirical comparison," Computational Statistics, Springer, vol. 29(3), pages 407-430, June.
- Sangjin Kim & Jong-Min Kim, 2019. "Two-Stage Classification with SIS Using a New Filter Ranking Method in High Throughput Data," Mathematics, MDPI, vol. 7(6), pages 1-16, May.
- Anders Bredahl Kock, 2012. "On the Oracle Property of the Adaptive Lasso in Stationary and Nonstationary Autoregressions," CREATES Research Papers 2012-05, Department of Economics and Business Economics, Aarhus University.
- Hung Hung & Su‐Yun Huang, 2019. "Sufficient dimension reduction via random‐partitions for the large‐p‐small‐n problem," Biometrics, The International Biometric Society, vol. 75(1), pages 245-255, March.
- Loann David Denis Desboulets, 2018.
"A Review on Variable Selection in Regression Analysis,"
Econometrics, MDPI, vol. 6(4), pages 1-27, November.
- Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Post-Print hal-01954386, HAL.
- Li, Xinyi & Wang, Li & Nettleton, Dan, 2019. "Sparse model identification and learning for ultra-high-dimensional additive partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 204-228.
- Li, Peili & Jiao, Yuling & Lu, Xiliang & Kang, Lican, 2022. "A data-driven line search rule for support recovery in high-dimensional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
- Zhang, Jing & Wang, Qihua & Kang, Jian, 2020. "Feature screening under missing indicator imputation with non-ignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
- Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022.
"On LASSO for predictive regression,"
Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
- Ji Hyung Lee & Zhentao Shi & Zhan Gao, 2018. "On LASSO for Predictive Regression," Papers 1810.03140, arXiv.org, revised Feb 2021.
- Ian W. McKeague & Min Qian, 2015. "An Adaptive Resampling Test for Detecting the Presence of Significant Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1422-1433, December.
- Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015.
"A lava attack on the recovery of sums of dense and sparse signals,"
CeMMAP working papers
CWP56/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," CeMMAP working papers 56/15, Institute for Fiscal Studies.
- Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," CeMMAP working papers CWP05/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," Papers 1502.03155, arXiv.org, revised Mar 2015.
- Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," CeMMAP working papers 05/15, Institute for Fiscal Studies.
- Lu, Jun & Lin, Lu, 2018. "Feature screening for multi-response varying coefficient models with ultrahigh dimensional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 242-254.
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:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49804-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.