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We also found that incorporating natively disordered regions predicted by DISOPRED improves the prediction performance further even further.
Remarkably, we found that the use of predicted solvent accessibility information considerably improves the prediction accuracy, implying the important role solvent accessibility plays in predicting residue depth profiles.
Several other genomic analysis studies have demonstrated that while an individual genomic feature may not predict the outcome, combining multiple genomic features improves the prediction accuracy [ 24- 29].
To show how much the new parameterization improves the prediction accuracy, we used the miRDeep with the default parameters to predict miRNA candidates.
This work improves the prediction of polymer viscosity in underground environment.
This in turn improves the prediction of the boundary shear stress.
Proposed method improves the prediction accuracy by 0.9%, sensitivity and specificity of 0.4 and 1.7%.
Finally, considering both FNDF and CRDM improves the prediction of mean pH.
This improves the prediction capabilities of an embedded extended Kalman filter that serves for data fusion.
Performances of the new method significantly improves the prediction of minimum fluidization velocity.
We further propose an parameter-dependent index, which considerably improves the prediction accuracy.
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