Partitioning of functional gene expression data using principal points.

ABSTRACT:

Background

DNA microarrays offer motivation and hope for the simultaneous study of variations in multiple genes. Gene expression is a temporal process that allows variations in expression levels with a characterized gene function over a period of time. Temporal gene expression curves can be treated as functional data since they are considered as independent realizations of a stochastic process. This process requires appropriate models to identify patterns of gene functions. The partitioning of the functional data can find homogeneous subgroups of entities for the massive genes within the inherent biological networks. Therefor it can be a useful technique for the analysis of time-course gene expression data. We propose a new self-consistent partitioning method of functional coeffici

SUBMITTER: Kim J 

PROVIDER: S-EPMC5639779 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature