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RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

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Academic year: 2017

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Figure 4 shows the mean correlation values inferred by RegnANN (left) and the obtained interaction among genes for correlation greater than 0:50 (right).
Figure 8 shows the mean correlation values for the 10 data generation/inference iterations (left) and the obtained interaction among genes for correlation strictly greater than 0:50 (right).
Figure 17 summarizes the AUC MR scores obtained by the three inference algorithms varying the number of nodes in the syntheticFigure 9
Figure 18 shows the mean AUC MR scores for ARACNE, CLR and RegnANN on a synthetic Barabasi network generated using a power-law exponent equal to 2 and on a synthetic Erdo¨s-Re´nyi network with mean degree equal to 2
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