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Table 7 Comparing the performance of DEEPLYESSENTIAL and Ning et al. and Nigatu et al. on their respective datasets [16, 35]; numbers in boldface indicate the best performance

From: DeeplyEssential: a deep neural network for predicting essential genes in microbes

Method Clustering method AUC
Ning et al. 2014 CD-HIT 0.758
DeeplyEssential OrthoMCL 0.818
Nigatu et al. 2017 Kullback-Leibler divergence 0.650
DeeplyEssential OrthoMCL 0.840