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Table 4 Average text scores (± standard deviation) of DeepSets and DGN variants when using one-hot encodings as protein identifiers

From: Sensitivity analysis on protein-protein interaction networks through deep graph networks

Model

UC

MCC

F1

ACC

AUROC

DeepSets

1

\(.498\scriptstyle \pm .009\)

\(.661\scriptstyle \pm .008\)

\(.778\scriptstyle \pm .007\)

\(.834\scriptstyle \pm .003\)

DGN

1

\(\mathbf {.764\scriptstyle \pm .008}\)

\(\mathbf {.842\scriptstyle \pm .005}\)

\(\mathbf {.896\scriptstyle \pm .004}\)

\(\mathbf {.952\scriptstyle \pm .001}\)

DeepSets

2

\(.427\scriptstyle \pm .032\)

\(.595\scriptstyle \pm .047\)

\(.759\scriptstyle \pm .015\)

\(.794\scriptstyle \pm .026\)

DGN

2

\(\mathbf {.543\scriptstyle \pm .043}\)

\(\mathbf {.690\scriptstyle \pm .039}\)

\(\mathbf {.801\scriptstyle \pm .018}\)

\(\mathbf {.851\scriptstyle \pm .014}\)

DeepSets

3

\(.146\scriptstyle \pm .076\)

\(.284\scriptstyle \pm .107\)

\(.677\scriptstyle \pm .028\)

\(.622\scriptstyle \pm .042\)

DGN

3

\(\mathbf {.253\scriptstyle \pm .058}\)

\(\mathbf {.486\scriptstyle \pm .047}\)

\(\mathbf {.677\scriptstyle \pm .027}\)

\(\mathbf {.651\scriptstyle \pm .049}\)