Adaptively capturing the heterogeneity of expression for cancer biomarker identification.

ABSTRACT:

Background

Identifying cancer biomarkers from transcriptomics data is of importance to cancer research. However, transcriptomics data are often complex and heterogeneous, which complicates the identification of cancer biomarkers in practice. Currently, the heterogeneity still remains a challenge for detecting subtle but consistent changes of gene expression in cancer cells.

Results

In this paper, we propose to adaptively capture the heterogeneity of expression across samples in a gene regulation space instead of in a gene expression space. Specifically, we transform gene expression profiles into gene regulation profiles and mathematically formulate gene regulation probabilities (GRPs)-based statistics for characterizing differential expression of genes between tumor and norm

SUBMITTER: Xie XP 

PROVIDER: S-EPMC6215657 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature