Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size.

ABSTRACT:

Background

Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions

SUBMITTER: Guo W 

PROVIDER: S-EPMC5477119 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature