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The results show that the RF model achieved a lower mean square error.
The neural network model achieved a better performance than the linear model.
Prediction of the probability of graft survival from the model, achieved a good match with the observed survival of the separate dataset, with a correlation of r = 0.998 for LM and r = 0.984 for TBM.
The proposed model achieved a linear correlation index r2 = 0.9952 and r2 = 0.9710 over experimental data for free and immobilized cells respectively.
The phenology-guided composite image from the MSAC model achieved a greater saltcedar mapping accuracy than any single Landsat image in 2004.
Their forecast model achieved a mean absolute percentage error (MAPE) of 9.9%.
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The model achieved an AUC = 0.65, 0.65, 0.64 for Tcfcp2l1, Nr5f2a, and Stat3, respectively.
Interestingly, our model achieved an equal AUC = 0.84 for H3K27ac and Cdk8.
Using only the motif information, the model achieved an AUC = 0.72.
The model achieved an AUC = 0.58 for phastCons, 0.63 for GC content, and 0.64 for repeat fraction (Fig. 3d).
By combining the GC content and phastCons the model achieved an AUC = 0.71, and by combining GC content and repeat fraction it achieved an AUC = 0.76.
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