Inferring genetic interactions via a nonlinear model and an optimization algorithm.

ABSTRACT:

Background

Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target.

Results

An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA cons

SUBMITTER: Chen CM 

PROVIDER: S-EPMC2848194 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature