Graph-based prediction of Protein-protein interactions with attributed signed graph embedding

BMC Bioinformatics
Fang YangHuakang Lin

Abstract

Protein-protein interactions (PPIs) are central to many biological processes. Considering that the experimental methods for identifying PPIs are time-consuming and expensive, it is important to develop automated computational methods to better predict PPIs. Various machine learning methods have been proposed, including a deep learning technique which is sequence-based that has achieved promising results. However, it only focuses on sequence information while ignoring the structural information of PPI networks. Structural information of PPI networks such as their degree, position, and neighboring nodes in a graph has been proved to be informative in PPI prediction. Facing the challenge of representing graph information, we introduce an improved graph representation learning method. Our model can study PPI prediction based on both sequence information and graph structure. Moreover, our study takes advantage of a representation learning model and employs a graph-based deep learning method for PPI prediction, which shows superiority over existing sequence-based methods. Statistically, Our method achieves state-of-the-art accuracy of 99.15% on Human protein reference database (HPRD) dataset and also obtains best results on Database ...Continue Reading

References

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Citations

Apr 4, 2021·International Journal of Molecular Sciences·Noam AuslanderEugene V Koonin
Apr 13, 2021·Expert Opinion on Drug Discovery·Finlay MacLean

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Methods Mentioned

BETA
Co-Immunoprecipitation
Interactions prediction
feature extractions
feature extraction
in silico methods

Software Mentioned

Lasagna
Tensorflow
DeepPPI
VGAE
Ensemble Deep Neural Networks
EnsDNN

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