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Table 5 Performance comparison using different architectures for meta-classifier based on fivefold cross-validation results (mean ± one standard deviation)

From: pLMSNOSite: an ensemble-based approach for predicting protein S-nitrosylation sites by integrating supervised word embedding and embedding from pre-trained protein language model

Model

ACC

SN

SP

MCC

ANN

0.727 ± 0.017

0.769 ± 0.016

0.685 ± 0.033

0.4573 ± 0.032

LR

0.703 ± 0.014

0.740 ± 0.017

0.665 ± 0.028

0.407 ± 0.027

SVM

0.719 ± 0.021

0.807 ± 0.029

0.631 ± 0.017

0.445 ± 0.043

RF

0.724 ± 0.010

0.771 ± 0.026

0.678 ± 0.022

0.451 ± 0.021

XGBoost

0.697 ± 0.006

0.735 ± 0.014

0.660 ± 0.022

0.396 ± 0.011

  1. The highest value in each column is highlighted in bold
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BMC Bioinformatics

ISSN: 1471-2105

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