Genomic distance entrained clustering and regression modelling highlights interacting genomic regions contributing to proliferation in breast cancer.

ABSTRACT:

Background

Genomic copy number changes and regional alterations in epigenetic states have been linked to grade in breast cancer. However, the relative contribution of specific alterations to the pathology of different breast cancer subtypes remains unclear. The heterogeneity and interplay of genomic and epigenetic variations means that large datasets and statistical data mining methods are required to uncover recurrent patterns that are likely to be important in cancer progression.

Results

We employed ridge regression to model the relationship between regional changes in gene expression and proliferation. Regional features were extracted from tumour gene expression data using a novel clustering method, called genomic distance entrained agglomerative (GDEC) clustering. Using

SUBMITTER: Dexter TJ 

PROVIDER: S-EPMC2946304 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature