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Using only QSAR predictions, accuracy is ~80-85 ~80-85sensitivity being slightly lower than specificity.
However, predictions accuracy depends on the platform, for instance on x86 architectures prediction errors ranges between 1-20%.
A common practice to boost target gene predictions accuracy is to use several prediction algorithms and then to combine their predictions.
For the per residue evaluations, we use three criteria to assess binary predictions: accuracy, true-positive rate (TPR) and false-positive rate (FPR).
To evaluate these variants, we calculate accuracy from the counts of true (T) and false (F) predictions: Accuracy = T/ T+F) To penalise variants for potential over-prediction, we calculate both a permissive and a strict accuracy.
Genomic predictions accuracy and bias for the Jersey validation population using different combinations of reference datasets (bulls or bulls and cows), method of prediction (GBLUP or BayesR), breed composition in the reference population (single or both breeds), and with or without inclusion of the polygenic term in the prediction.
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Prediction Accuracy.
Figure 12 Prediction accuracy.
Figure 13 Prediction accuracy analysis.
Fig. 9 Improved prediction accuracy.
Fig. 10 Prediction accuracy with trajectory length.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com