Incorporating prior information in gene expression network-based cancer heterogeneity analysis.

ABSTRACT: Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the l

SUBMITTER: Li R 

PROVIDER: S-EPMC12550826 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature