Abstract
BackgroundProtein–protein interactions (PPIs) are of great importance in cellular systems of organisms, since they are the basis of cellular structure and function and many essential cellular processes are related to that. Most proteins perform their functions by interacting with other proteins, so predicting PPIs accurately is crucial for understanding cell physiology.ResultsRecently, graph convolutional networks (GCNs) have been proposed to capture the graph structure information and generate representations for nodes in the graph. In our paper, we use GCNs to learn the position information of proteins in the PPIs networks graph, which can reflect the properties of proteins to some extent. Combining amino acid sequence information and position information makes a stronger representation for protein, which improves the accuracy of PPIs prediction.ConclusionIn previous research methods, most of them only used protein amino acid sequence as input information to make predictions, without considering the structural information of PPIs networks graph. We first time combine amino acid sequence information and position information to make representations for proteins. The experimental results indicate that our method has strong competitiveness compared with several sequence-based methods.
Highlights
Protein–protein interactions (PPIs) are of great importance in cellular systems of organisms, since they are the basis of cellular structure and function and many essential cellular processes are related to that
PPIs play an important role in cellular systems of organisms, most proteins perform their functions by interacting with other proteins, so information about the PPIs can help us better understand the function of proteins [1]
Our main contributions can be summarized as follows: (1) We use graph convolutional networks (GCNs) to capture the position information of the proteins in the PPIs networks graph, which can reflect the properties of proteins to some extent
Summary
Protein–protein interactions (PPIs) are of great importance in cellular systems of organisms, since they are the basis of cellular structure and function and many essential cellular processes are related to that. Most proteins perform their functions by interacting with other proteins, so predicting PPIs accurately is crucial for understanding cell physiology. High-throughput biological techniques and large-scale experimental approaches for PPIs identification have achieved tremendous development, lots of PPIs data from different organisms has been discovered by researchers [2]. Calculation-based methods can solve the problem to a certain extent, and provide reference and guidance for the biological experiment design, which are helpful for laboratory validation
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