Tightly integrated genomic and epigenomic data mining using tensor decomposition.

ABSTRACT:

Motivation

Complex diseases such as cancers often involve multiple types of genomic and/or epigenomic abnormalities. Rapid accumulation of multiple types of omics data demands methods for integrating the multidimensional data in order to elucidate complex relationships among different types of genomic and epigenomic abnormalities.

Results

In the present study, we propose a tightly integrated approach based on tensor decomposition. Multiple types of data, including mRNA, methylation, copy number variations and somatic mutations, are merged into a high-order tensor which is used to develop predictive models for overall survival. The weight tensors of the models are constrained using CANDECOMP/PARAFAC (CP) tensor decomposition and learned using support tensor machine regression

SUBMITTER: Fang J 

PROVIDER: S-EPMC6298052 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature