Effects of pseudobulk and Gaussian noise on the application of the dynamical network biomarker theory to single-cell RNA-seq data-Reference-Cited by-同舟云学术

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Effects of pseudobulk and Gaussian noise on the application of the dynamical network biomarker theory to single-cell RNA-seq data

Published:2024 Issue:1 Volume:11 Page:147-157
ISSN:2188-5303
Container-title:Journal of Advanced Simulation in Science and Engineering
language:en
Short-container-title:Journal of Advanced Simulation in Science and Engineering

Author:

Yonezawa Shota¹²,Haruki Takayuki²³,Koizumi Keiichi²⁴,Watanabe Tomonobu M.⁵,Abe Kuniya⁶,Tada Yuhki⁶,Horita Yuukou³

Affiliation:

1. Graduate School of Science and Engineering, University of Toyama

2. Research Center for Pre-Disease Science, University of Toyama

3. Faculty of Sustainable Design, University of Toyama

4. Division of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama

5. RIKEN Center for Biosystems Dynamic Research

6. RIKEN BioResource Research Center

Publisher

Japan Society for Simulation Technology

Link

https://www.jstage.jst.go.jp/article/jasse/11/1/11_147/_pdf

Reference8 articles.

1. [1] M. Scheffer et al.: Early-warning signals for critical transitions, Nature, 461:7260 (2009), 53–59.

2. [2] L. Chen et al.: Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers, Scientiﬁc Reports, 2:1 (2012), 342.

3. [3] K. Aihara et al.: Dynamical network biomarkers: Theory and applications, Gene, 808 (2022), 145997.

4. [4] K. Koizumi et al.: Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers, Scientiﬁc Reports, 9:1 (2019), 8767.

5. [5] T. Haruki et al.: Application of the Dynamical Network Biomarker Theory to Raman Spectra, Biomolecules, 12:12 (2022).

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