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Fig. 2 | BMC Bioinformatics

Fig. 2

From: EnsInfer: a simple ensemble approach to network inference outperforms any single method

Fig. 2

The performance of various network inference methods on three different species, from top to bottom: a B. subtilis gene regulatory network using bulk RNA-seq expression data [25]; an Arabidopsis network using bulk RNA-seq expression data [26]; a mouse Embryonic Stem Cell functional interaction network using single cell RNA-seq data [27] and a human Embryonic Stem Cell ChIP-seq network using single cell RNA-seq expression data [28]. Inference performance was measured using the ratio of the AUPRC of each inference method divided by that of a random predictor. Gold standard priors from each of the three species were split into a random 2:1 training/testing configuration. Ensemble models along with base inference methods that are able to incorporate prior information were trained using training gold standard priors. Then all inference results were applied using the testing subset of the gold standard data yielding an AUPRC ratio using 20 random training/testing split setups. The mean AUPRC ratio of each method on the test data among these 20 experiments is represented in the bar chart. All four ensemble models were evaluated here with base inference methods, and each of them was trained with the three worst performing base inference methods in training set (see the A series histogram) or without the three worst performing base inference methods (B series histogram). Asterisks indicate a statistically significant (p-value below 0.05 in non-parametric paired tests) improvement compared to the best level 1 inference method (and compared to the average ranking approach). Overall, Naive Bayes performs best, but in some cases Adaptive Boosting and Random Forests do almost as well

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