Extended Data Fig. 5: Performance comparison of algorithms used to develop computational models that predict the activities of the small Cas9s. | Nature Methods

Extended Data Fig. 5: Performance comparison of algorithms used to develop computational models that predict the activities of the small Cas9s.

From: Massively parallel evaluation and computational prediction of the activities and specificities of 17 small Cas9s

Extended Data Fig. 5

Heatmaps showing correlations between the measured and computationally predicted indel frequencies. Average Pearson (top) and Spearman (bottom) correlation coefficients were calculated from five-fold cross-validation. The algorithms that showed the highest average correlation coefficients are shown in bold. XGBoost, extreme gradient boosting; Boosted RT, gradient-boosted regression trees; Lasso, L1-regularized linear regression; Ridge, L2-regularized linear regression; Elastic Net, L1 and L2-regularized linear regression; RF, random forest; SVM, support vector machine.

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