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Fig. 3 | BMC Bioinformatics

Fig. 3

From: Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes

Fig. 3

The efficiency of bottom-up GGM algorithm. a. ROC curves of bottom-up GGM algorithm resulted from five testing data sets, each contains 300 TFs, and 25 pathway genes. b. ROC curves of ARACNE resulted from five testing data sets, each contains 300 TFs, and 25 pathway genes. c. F scores of bottom-up GGM and ARACNE in terms of different TF-cutoffs. d. The relationship between true positive rates (TPR) and different numbers of TFs as inputs. The TPR of ARACNE is uniform because it captured just one positive for various numbers of TF inputs varying from 44 to 1500

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BMC Bioinformatics

ISSN: 1471-2105

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