Skip to main content

Table 1 Comparison of performance

From: Development and validation of a machine learning algorithm prediction for dense granule proteins in Apicomplexa

Method

AUPRC

Precision

Accuracy

AUC

F1

Recall

Specificity

MVA-GCN

0.9487a

1.0000a

0.9658b

0.9673a

0.8181b

0.6923b

1.0000a

GCN-CKSAAP

0.8456b

0.9250b

0.9508

0.9508b

0.7531

0.6388

0.9929b

GCN-CTDC

0.8071

0.9032

0.9622

0.8885

0.8115

0.6855

0.9893

GCN-CTDT

0.8310

0.9113

0.9663a

0.9113

0.8372a

0.7230a

0.9856

GCN-TPC

0.7788

0.8541

0.9343

0.9268

0.6578

0.5413

0.9870

  1. We calculated each of the AUPRC, AUC, accuracy, precision, recall, and F1-score models. MVA-GCN had the highest precision, followed by AUPRC
  2. aHighest value of each indicator
  3. bSecond-best value of each indicator