DeepPPI: Boosting Prediction of Protein–Protein Interactions with Deep Neural Networks

The complex language of eukaryotic
gene expression remains incompletely
understood. Despite the importance suggested by many proteins variants
statistically associated with human disease, nearly all such variants
have unknown mechanisms, for example, protein–protein interactions
(PPIs). In this study, we address this challenge using a recent machine
learning advance-deep neural networks (DNNs). We aim at improving
the performance of PPIs prediction and propose a method called DeepPPI
(Deep neural networks for Protein–Protein Interactions prediction),
which employs deep neural networks to learn effectively the representations
of proteins from common protein descriptors. The experimental results
indicate that DeepPPI achieves superior performance on the test data
set with an Accuracy of 92.50%, Precision of 94.38%, Recall of 90.56%,
Specificity of 94.49%, Matthews Correlation Coefficient of 85.08%
and Area Under the Curve of 97.43%, respectively. Extensive experiments
show that DeepPPI can learn useful features of proteins pairs by a
layer-wise abstraction, and thus achieves better prediction performance
than existing methods. The source code of our approach can be available
via http://ailab.ahu.edu.cn:8087/DeepPPI/index.html.