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Figure 9 | EURASIP Journal on Bioinformatics and Systems Biology

Figure 9

From: Modelling Transcriptional Regulation with a Mixture of Factor Analyzers and Variational Bayesian Expectation Maximization

Figure 9

Out-of-sample TF regulatory network reconstruction for yeast. Receiver operating characteristic (ROC) curves obtained for S. cerevisiae with three different methods: (1) solid line: the proposed MFA-VBEM method, based on the work of Beal [23], and extended as described in Section 3; (2) dashed line: the Bayesian FA model with Gibbs sampling, as proposed in Sabatti and James [16]; and (3) dotted line: maximum likelihood FA with the EM algorithm of Ghahramani and Hinton [24] and a subsequent varimax rotation [39] of the loading matrix towards maximum sparsity, as proposed in Pournara and Wernisch [18]. The subfigures show the out-of-sample performance on an independent test set containing genes not used for training (network prediction). From left to right, the models were trained using 40%, 60% and 80% of data.

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