Inference of causal networks from time-varying transcriptome data via sparse coding.

ABSTRACT: Temporal analysis of genome-wide data can provide insights into the underlying mechanism of the biological processes in two ways. First, grouping the temporal data provides a richer, more robust representation of the underlying processes that are co-regulated. The net result is a significant dimensional reduction of the genome-wide array data into a smaller set of vocabularies for bioinformatics analysis. Second, the computed set of time-course vocabularies can be interrogated for a potential causal network that can shed light on the underlying interactions. The method is coupled with an experiment for investigating responses to high doses of ionizing radiation with and without a small priming dose. From a computational perspective, inference of a causal network can rapidly become computat

SUBMITTER: Zhang K 

PROVIDER: S-EPMC3423420 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature