Cargando…
KBoost: a new method to infer gene regulatory networks from gene expression data
Reconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, ma...
Autores principales: | Iglesias-Martinez, Luis F., De Kegel, Barbara, Kolch, Walter |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322418/ https://www.ncbi.nlm.nih.gov/pubmed/34326402 http://dx.doi.org/10.1038/s41598-021-94919-6 |
Ejemplares similares
-
BGRMI: A method for inferring gene regulatory networks from time-course gene expression data and its application in breast cancer research
por: Iglesias-Martinez, Luis F., et al.
Publicado: (2016) -
An algebra-based method for inferring gene regulatory networks
por: Vera-Licona, Paola, et al.
Publicado: (2014) -
Benchmarking Gene Regulatory Network Inference Methods on Simulated and Experimental Data
por: Saint-Antoine, Michael, et al.
Publicado: (2023) -
Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data
por: Chen, Shuonan, et al.
Publicado: (2018) -
Integration of Steady-State and Temporal Gene Expression Data for the Inference of Gene Regulatory Networks
por: Wang, Yi Kan, et al.
Publicado: (2013)