Inferring gene regulatory networks with graph convolutional network based on causal feature reconstruction
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-71864-8.pdf
Reference43 articles.
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2. Ahmed, S. S., Roy, S. & Kalita, J. Assessing the effectiveness of causality inference methods for gene regulatory networks. IEEE/ACM Trans. Comput. Biol. Bioinform. 17, 56–70. https://doi.org/10.1109/TCBB.2018.2853728 (2020).
3. Ma, Q. et al. Uncovering mechanisms of transcriptional regulations by systematic mining of cis regulatory elements with gene expression profiles. BioData Min. 1. https://doi.org/10.1186/1756-0381-1-4 (2008).
4. Park, J. et al. UPF1/SMG7-dependent microRNA-mediated gene regulation. Nat Commun. 10. https://doi.org/10.1038/s41467-019-12123-7 (2019).
5. Ma, B., Fang, M. & Jiao, X. Inference of gene regulatory networks based on nonlinear ordinary differential equations. Bioinformatics. 36, 4885–4893. https://doi.org/10.1093/bioinformatics/btaa032 (2020).
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