Incorporating inter-relationships between different levels of genomic data into cancer clinical outcome prediction.

ABSTRACT: In order to improve our understanding of cancer and develop multi-layered theoretical models for the underlying mechanism, it is essential to have enhanced understanding of the interactions between multiple levels of genomic data that contribute to tumor formation and progression. Although there exist recent approaches such as a graph-based framework that integrates multi-omics data including copy number alteration, methylation, gene expression, and miRNA data for cancer clinical outcome prediction, most of previous methods treat each genomic data as independent and the possible interplay between them is not explicitly incorporated to the model. However, cancer is dysregulated by multiple levels in the biological system through genomic, epigenomic, transcriptomic, and proteomic level. Thus

SUBMITTER: Kim D 

PROVIDER: S-EPMC4664202 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature