Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder

Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this p...

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Vydané v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 20; číslo 5; s. 3182 - 3194
Hlavní autori: Luo, Xin, Wang, Liwei, Hu, Pengwei, Hu, Lun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Amino acid sequence
Artificial neural networks
Biological system modeling
Computational modeling
Feature extraction
Graph neural networks
Neural networks
PPI network
PPI prediction
Prediction algorithms
Predictions
Predictive models
Principal components analysis
Protein interaction
Protein sequences
Protein-protein Interaction
Proteins
Proteomics
Task analysis
Variational graph autoencoder
ISSN:1545-5963, 1557-9964, 1557-9964
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Shrnutí:Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.
Bibliografia:ObjectType-Article-1
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2023.3273567