Figure 12.

Classification of the 14 experiments used in the sign-inference process for the global transcriptional network (2419 nodes, 4344 edges). The experiments are represented by their identifier (see Table 2). Each experiment has a twofold contribution: it spots inconsistent modules (MBM that are further excluded from inference) and it predicts interaction roles. Some experiments have more predictive power, just because they include more genes. In order to normalise the predictive power, we divided the percentage of predictions by the percentage of observed nodes. For each experiment we have estimated: (A) Number of significant (2-fold) up/down-regulated genes. (B) Percentage of edges in the spotted MBMs of type II-IV divided by the percentage of observed genes. (C) Percentage of inferred signs divided by the percentage of observed genes. (D) Real contribution of each experiment, calculated by subtracting C (inference) from B (eliminated inconsistency); negative values correspond to experiments whose main role is to spot ambiguities.

Veber et al. BMC Bioinformatics 2008 9:228   doi:10.1186/1471-2105-9-228
Download authors' original image