A subnetwork-based framework for prioritizing and evaluating prognostic gene modules from cancer transcriptome data

Cancer prognosis prediction is critical to the clinical decision-making process. Currently, the high availability of transcriptome datasets allows us to extract the gene modules with promising prognostic values. However, the biomarker identification is greatly challenged by tumor and patient heterog...

Descripción completa

Detalles Bibliográficos
Autores principales: Cao, Biwei, Patel, Krupal B., Li, Tingyi, Yao, Sijie, Chung, Christine H., Wang, Xuefeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845797/
https://www.ncbi.nlm.nih.gov/pubmed/36685033
http://dx.doi.org/10.1016/j.isci.2022.105915

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845797/
https://www.ncbi.nlm.nih.gov/pubmed/36685033
http://dx.doi.org/10.1016/j.isci.2022.105915

Ejemplares similares