Predicting gene targets of perturbations via network-based filtering of mRNA expression compendia.

ABSTRACT:

Motivation

DNA microarrays are routinely applied to study diseased or drug-treated cell populations. A critical challenge is distinguishing the genes directly affected by these perturbations from the hundreds of genes that are indirectly affected. Here, we developed a sparse simultaneous equation model (SSEM) of mRNA expression data and applied Lasso regression to estimate the model parameters, thus constructing a network model of gene interaction effects. This inferred network model was then used to filter data from a given experimental condition of interest and predict the genes directly targeted by that perturbation.

Results

Our proposed SSEM-Lasso method demonstrated substantial improvement in sensitivity compared with other tested methods for predicting the targets of p

SUBMITTER: Cosgrove EJ 

PROVIDER: S-EPMC2732281 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature