An integer optimization algorithm for robust identification of non-linear gene regulatory networks.

ABSTRACT:

Background

Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction.

Results

We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the

SUBMITTER: Chemmangattuvalappil N 

PROVIDER: S-EPMC3444924 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature