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The new approach that we present here, SW, extends GeneMANIA algorithm (Mostafavi et al., 2008) that was previously shown to have the state-of-art performance on yeast and mouse benchmark datasets (Mostafavi et al., 2008; Pena-Castillo et al., 2008).
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Some benchmark datasets are used to evaluate the proposed algorithm.
We used two benchmark datasets to evaluate our system.
We extensively evaluate our approach on 13 benchmark datasets and a fault diagnosis dataset.
Our approach outperforms the state-of-the-arts on three benchmark datasets.
Existing benchmark datasets overrepresent lighter men in particular and lighter individuals in general.
We compare our method with 14 saliency models on 6 public eye tracking benchmark datasets.
Algorithms are only as good as their benchmark datasets, and those datasets reflect their creators' biases (conscious or not).
We have conducted the experiments on several benchmark datasets.
We tested the performance of the best classifiers only on the benchmark datasets (i.e. Datasets S3 and S4).
We thank Guan Ning Lin for sharing phylogeny benchmark datasets and providing ComPhy software.
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