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In summary, the signal peptide predictors provided the initial candidates of secreted proteins.
In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance.
Forward stepwise logistic regression then was used to determine which potentially significant predictors provided the most explanatory power with regard to treatment response.
A single regression with both s and d as predictors gave the linear gradients; separate regressions with either s or d as predictors provided the linear differentials.
MLR modeling of NSLN status that was informed by CART in its selection of predictors provided the most accurate cross-validated technique for predicting NSLN metastases for patients with known angiolymphatic invasive status, with accuracy superior to boosted CART, RP-ROC, and the Memorial Sloan-Kettering Breast Cancer NoMemorial Sloan-Kettering
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Per-residue predictors provide the distribution of the propensity for intrinsic disorder over the amino acid sequence, whereas binary classifiers identify entire protein as wholly ordered or wholly disordered.
The inter-sample model predictor provides the observer with estimates of the unavailable outputs, and its predictions are corrected each time that a measurement becomes available.
We can see that the proposed predictor provides the smallest error power and the prediction errors are closer to zero.
I am generally quite convinced that the pkaPS predictor provides the current state-of-the-art for PKA phosporylation site prediction.
The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode.
Consequently, we observed that a combination of structural and functional information within a logistic regression based predictor provides the best performance and is more indicative of GIs than either property by itself.
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CEO of Professional Science Editing for Scientists @ prosciediting.com