Fig. 2: Testing the accuracy of the prediction model with the GRACEv2 collection. | Nature Communications

Fig. 2: Testing the accuracy of the prediction model with the GRACEv2 collection.

From: Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Fig. 2

a Distribution of prediction scores for the 98 experimentally confirmed essential genes and 768 non-essential genes from the validation candidates (GRACEv2 strains). b Precision-recall curve of the random forest model derived from the whole GRACE set and tested on the GRACEv2 experimental validation set. The default stringent cutoff score for essential gene predictions results in a precision of 0.64 and a recall of 0.76, with an average precision score of 0.66. c Essential genes are enriched in specific functional clusters. Clusters were generated by UMAP embedding of co-expression and functional enrichment was determined by GO term analysis. Source data are provided as a Source Data file.

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