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Table 2 P-value comparison of MFSynDCP and comparative methods using t-test

From: MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction

Methods

p-value t-test of AUROC

p-value t-test of AUPR

p-value t-test of ACC

MGAE-DC

\(7.28\times {10}^{-3}\)

\(7.73\times {10}^{-27}\)

\(4.72\times {10}^{-3}\)

SDCNet

\(4.42\times {10}^{-2}\)

\(5.13\times {10}^{-3}\)

\(3.56\times {10}^{-5}\)

DFFNDDS

\(2.13\times {10}^{-6}\)

\(1.01\times {10}^{-11}\)

\(5.03\times {10}^{-13}\)

XGBoost

\(1.36\times {10}^{-3}\)

\(4.87\times {10}^{-4}\)

\(1.22\times {10}^{-5}\)

PRODeepDyn

\(7.22\times {10}^{-9}\)

\(3.73\times {10}^{-3}\)

\(6.71\times {10}^{-3}\)

TranSynergy

\(9.63\times {10}^{-9}\)

\(7.67\times {10}^{-12}\)

\(1.15\times {10}^{-3}\)

DTF

\(8.72\times {10}^{-10}\)

\(3.25\times {10}^{-12}\)

\(1.71\times {10}^{-10}\)

DeepSynergy

\(5.34\times {10}^{-13}\)

\(4.69\times {10}^{-13}\)

\(3.28\times {10}^{-15}\)

GBM

\(3.19\times {10}^{-13}\)

\(7.21\times {10}^{-16}\)

\(7.98\times {10}^{-16}\)

Random Forest

\(6.01\times {10}^{-13}\)

\(3.54\times {10}^{-10}\)

\(1.66\times {10}^{-13}\)

Adaboost

\(3.37\times {10}^{-14}\)

\(9.82\times {10}^{-16}\)

\(5.16\times {10}^{-17}\)

MLP

\(9.69\times {10}^{-18}\)

\(8.65\times {10}^{-20}\)

\(1.77\times {10}^{-13}\)

SVM

\(6.76\times {10}^{-24}\)

\(1.49\times {10}^{-24}\)

\(1.52\times {10}^{-25}\)