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HNSPPI: a hybrid computational model combing network and sequence information for predicting protein–protein interaction by Shijie Xie; Xiaojun Xie; Xin Zhao; Fei Liu; Yiming Wang; Jihui Ping; Zhiwei Ji is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein–Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental result

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Author

Shijie Xie; Xiaojun Xie; Xin Zhao; Fei Liu; Yiming Wang; Jihui Ping; Zhiwei Ji

Publisher

Oxford University Press (OUP)

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)