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We evaluated the performance of our method by precision, recall, and F-measure: begin{aligned} P&= frac{TP}{TP + FP}, end{aligned} (9) begin{aligned} R&= frac{TP}{TP + FN}, end{aligned} (10) begin{aligned} F&= frac{2PR}{P + R}, end{aligned} (11 where TP, FP, and FN are the number of true positive, false positive, and false negative, respectively.
We have evaluated the performance of our method on three gene expression datasets, described as follows.
We evaluated the performance of our method on two real-life benchmark networks and compared with other related methods.
First, we evaluated the performance of our method conservatively by assuming that the original meta-analyses were comprehensive and complete.
We also evaluated the performance of our method against another method MIRZA [ 28], on the human dataset.
We evaluated the performance of our method (we denote as OptDis) against both single gene marker models and other subnetwork-based methods following the workflow presented by the MicroArray Quality Control (MAQC -II studies (Popovici, 2010; Shi, 2010).
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We evaluated the performances of our method by comparison with previously published work.
Cross-validation was performed to evaluate the performance of our method and for parameter optimization.
Hence, we additionally created synthetic ground truth data to further evaluate the performance of our method.
Here, we describe the simulation used to evaluate the performance of our method.
To evaluate the performance of our method, the proposed system is applied on medical images.
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