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SUPPLEMENTARY MATERIAL Comparing association network algorithms for reverse engineering
 

Summary: SUPPLEMENTARY MATERIAL
Comparing association network algorithms for reverse engineering
of large scale gene regulatory networks: synthetic vs real data
N. Soranzo, G. Bianconi and C. Altafini
SISSA-ISAS, International School for Advanced Studies
via Beirut 2-4, 34014 Trieste, Italy
Abdus Salam International Center for Theoretical Physics
Strada Costiera 11, 34014 Trieste, Italy
March 23, 2007
The material of this Supplement is divided into 3 Sections:
1. Synthetic data: integrates the content of Section 3.1 of the paper.
2. Influence of sparsity on the predictive power: compares inference on 2 networks with different
sparsity.
3. Comparing B-spline and Gaussian Kernel in the computation of I: evaluate how much the
matrix I changes with the algorithm chosen.
1 Synthetic data
This Section integrates the results obtained in Section 3.1 of the paper. For both AUC(ROC) and AUC(PvsR),
standard deviations (not shown) are around one order of magnitude smaller than the mean values, thus in-
dicating that the repetitions are substantially faithful.
For the random and scale-free networks reconstructed in Fig. 1 of the paper, Fig. S1 reports the average

  

Source: Altafini, Claudio - Functional Analysis Sector, Scuola Internazionale Superiore di Studi Avanzati (SISSA)

 

Collections: Engineering; Mathematics