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

Background 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 method...

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Veröffentlicht in:BMC bioinformatics Jg. 21; H. 1; S. 1 - 16
Hauptverfasser: Yang, Fang, Fan, Kunjie, Song, Dandan, Lin, Huakang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 21.07.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Accuracy
Algorithms
Bioinformatics
Biological activity
Biomedical and Life Sciences
Coders
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer applications
Datasets
Deep learning
Drosophila
E coli
Embedding
Escherichia coli
Experimental methods
Graph neural networks
Graphical representations
Identification methods
Learning algorithms
Life Sciences
Machine learning
Machine Learning and Artificial Intelligence in Bioinformatics
Methodology
Methodology Article
Microarrays
Network embedding
Neural networks
Predictions
Protein interaction
Protein-protein interaction
Protein-protein interactions
Proteins
Representation learning
Signal transduction
Social networks
Statistical methods
Variational graph auto-encoder
Representation learning
Network embedding
Protein-protein interaction
Variational graph auto-encoder
ISSN:1471-2105, 1471-2105
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Zusammenfassung:Background 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. Results 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 of Interacting Protein (DIP) Human, Drosophila , Escherichia coli ( E. coli ), and Caenorhabditis elegans ( C. elegan ) datasets. Conclusion Here, we introduce signed variational graph auto-encoder (S-VGAE), an improved graph representation learning method, to automatically learn to encode graph structure into low-dimensional embeddings. Experimental results demonstrate that our method outperforms other existing sequence-based methods on several datasets. We also prove the robustness of our model for very sparse networks and the generalization for a new dataset that consists of four datasets: HPRD, E.coli , C.elegan , and Drosophila .
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-03646-8