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Figure 4 | Theoretical Biology and Medical Modelling

Figure 4

From: Construction of gene regulatory networks using biclustering and bayesian networks

Figure 4

ROC and PR curves of different biclustering networks that have learned using Bayesian networks [29]. A performance comparison of networks generated from learning corresponding biclustering algorithms using the Bayesian networks method via the Friedman network [4] and the gold network retrieved by BioNetBuilder [33]. This figure shows that most of these networks contained few true positive edges. Neither the networks generated from different bicluster algorithms nor those generated from all biclustering networks (dashed line) perform well. ALL: This network is produced by integrating edges from all biclustering networks; Friedman Network [4]; SAMBA: This network is generated by integrating SAMBA [43] subnetworks; Kmeans: This network is generated by integrating k-means subnetworks; ISA: This network is generated by integrating ISA [31] subnetworks; OPSM: This network is generated by integrating OPSM [23] subnetworks; CC: This network is generated by integrating CC [24] subnetworks; Bivisu: This network is generated by integrating Bivisu [32] subnetworks; CMSBE: This network is generated by integrating MSBE [27] subnetworks.

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