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Fig 1.

Flowchart of the time-varying scale-free graphical lasso method for dynamic scale-free gene network modeling.

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Fig 2.

Analysis of data replicates generated by the R package huge [32] (a scale-free model) using different glasso variants and tvsfglasso at .

Different panels represent different binary classification metrics.

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Fig 2 Expand

Fig 3.

Analysis of data replicates generated by simulator described in [18] (a scale-free model) using different glasso variants and tvsfglasso at .

Different panels represent different binary classification metrics.

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Fig 4.

Analysis of data replicates generated by simulator described in [12] (a smooth scale-free model) using different glasso variants and tvglasso and tvsfglasso at .

Different panels represent different binary classification metrics.

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Fig 5.

Analysis of data replicates generated by simulator described in [33] (a super-hub model) using different glasso variants and tvglasso and tvsfglasso at .

Different panels represent different binary classification metrics.

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Fig 5 Expand

Fig 6.

Analysis of data replicates generated by simulator described in [13] (non-scale-free network) using different glasso variants and tvglasso and tvsfglasso at .

Different panels represent different binary classification metrics.

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Fig 7.

Analysis of data replicates generated by simulator described in [12] (smooth Erdős-Rényi graph) using different glasso variants and tvglasso and tvsfglasso at .

Different panels represent different binary classification metrics.

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Fig 8.

Estimated degree of the time-varying TFs network constructed using tvsfglasso at (A) for the whole network and (B) for the TFs with the greatest increases in degree, as determined by the highest slope coefficients , + time. Different line types represent different TFs.

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Fig 9.

Density estimates of degree distributions of the time-varying Drosophila melanogaster TF networks constructed with tvsfglasso at at time-points (A) 0h, and (B) 20h.

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Fig 10.

The neighborhood of BLIMP-1 in the Drosophila melanogaster networks at times-points (A) 16h, (B) 18h, and (C) 20h when the network is constructed using tvsfglasso at .

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