Stratification of Breast Cancer by Integrating Gene Expression Data and Clinical Variables.

ABSTRACT: Breast cancer is a heterogeneous disease. Although gene expression profiling has led to the definition of several subtypes of breast cancer, the precise discovery of the subtypes remains a challenge. Clinical data is another promising source. In this study, clinical variables are utilized and integrated to gene expressions for the stratification of breast cancer. We adopt two phases: gene selection and clustering, where the integration is in the gene selection phase; only genes whose expressions are most relevant to each clinical variable and least redundant among themselves are selected for further clustering. In practice, we simply utilize maximum relevance minimum redundancy (mRMR) for gene selection and k-means for clustering. We compare the results of our method with those of t

SUBMITTER: He Z 

PROVIDER: S-EPMC6385100 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature