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Table 3 The ROC score of methods for multi-modal data. NN is neural network model, NN 1/0 is neural network model that use binary representation of GO terms as features. (concat) is approach where GO terms and k-mer is concatenated as single vector to represent each protein, (multi-input) approach where GO terms and k-mer are used as two input to the model. The number of layers in neural network are three and the dimension of neurons in each layer are 128,64, and 1

From: Predicting metabolic pathway membership with deep neural networks by integrating sequential and ontology information

Methods

BP+k-mer

CC+k-mer

MF+k-mer

NN (concat)

0.954

0.874

0.907

NN (multi-input)

0.957

0.880

0.907

NN 1/0 (concat)

0.943

0.883

0.894

NN 1/0 (multi-input)

0.940

0.882

0.888

cosine (concat)

0.920

0.768

0.687

SVM (concat)

0.933

0.814

0.863

RF (concat)

0.923†

0.840

0.844

KNN (concat)

0.829

0.784

0.790

  1. †not significantly different than cosine method in each ontology