Transcription profiling of human 48 clinical breast cancer arrays to perform statistical identification of gene association by CID in application of constructing ER regulatory network

ABSTRACT: A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID), is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs) (X) and their downstream genes (Y) based on clinical data. More specifically, we use estrogen receptor alpha (ERalpha) as the variable X, and the analys

ORGANISM(S): Homo sapiens

SUBMITTER: Fon-Jou Hsieh 

PROVIDER: E-GEOD-17041 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress