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Naïve Bayes models of in vitro kinase-phosphosite assignment have important performance differences from PSSM-based methods.

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posted on 13.05.2022, 17:43 by Brandon M. Invergo

a) PSSM methods and Naïve Bayes perform similarly in cross-validation of multi-label kinase-substrate assignment via macro-averaged precision versus recall. The expanded Naïve Bayes+ model outperforms the other methods. Points indicate the scores at the cutoff that maximizes that macro-F1 score. Black error bars showing 95% confidence intervals at these points are indiscernible in most cases, indicating highly robust performance across cross-validation folds. b) The macro-averaged F1 scores behave differently with score/probability cutoff for scoring matrix-based models versus Naïve Bayes. PSSM and PFM-based models require a strictly defined cutoff. Naïve Bayes+ again outperforms the others and retains the same flat relationship with cutoff as basic Naïve Bayes. Points indicate the maximum value. Bands indicate the 95% confidence interval. Color assignments are the same as in (a). c) Example score distributions for a S/T kinase (AKT1) and a Y kinase (FYN) from one round of cross-validation. For S/T kinases, Naïve Bayes probabilities are largely distributed close to 0.0 and 1.0 while PSSM scores take more intermediate values, notably including scores for Y sites. Y kinases show better separation for both methods. d) Left: Logistic curves relating phosphoproteome-backed PSSM scores to Naïve Bayes probabilities. Each curve represents a fitted logistic function for each kinase. The color of the curve represents the number of kinase substrates used to fit each specificity model. Right: The fitted logistic curve parameters versus number of substrates. S/T and Y kinases have negative relationships between inflection point and numbers of substrates. e) Min-max normalization of PSSM scores does not produce a stable inflection point independent of the number of substrates.

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