Figure 1
From: SLDR: a computational technique to identify novel genetic regulatory relationships
An overview of the SLDR framework for identifying novel genetic regulatory relationships. The input data is a microarray data set. The intermediate process were involved data normalization, Pearson Correlation Model, cross-validatation in Yeast Fit Database, network generation, aggregation test, hypergeometric test, robustness detection and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) clustering. The output data are: a distribution curve of targets, two network of de-centric genetic regulatory relationship, and .txt or .csv files containing the activation/inhibition pairs of de-centric targets and the corresponding genetic regulatory networks.