Inferring essential proteins from centrality in interconnected multilayer networks

Haiyan Jin, ChenXing Zhang, Mengzhou Ma, Qianhua Gong, Liang Yu, Xingli Guo, Lin Gao and Bingbo Wang

Physica A: Statistical Mechanics and its Applications, 2020, vol. 557, issue C

Abstract: Computational tool for inferring essential genes and their coding essential proteins based on its topological features in biological network is crucial for insights into vital and disease mechanism of organisms. The problem of how to quantify this correspondence via network centralities in an incomplete protein interaction network (PIN), such as that of human, remains open. Here we develop Random Walk Occupation (RWO) augmented method in interconnected multilayer network model to disclose topological characteristics of essential proteins. The peculiar non-hub essential proteins can be revealed in yeast–human interconnected networks. Statistically RWO augmented method performs well, and improves the prediction results in incomplete human PIN and maintains noticeable performance in well-tested yeast PIN. Biologically, the detected non-hub essential proteins really participate in some vital biological function. Overall, we present a systematic method for deeper understanding and effective identification of essential proteins.

Keywords: Random walk; Centrality; Essential proteins; Interconnected multilayer network (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:557:y:2020:i:c:s0378437120304428

DOI: 10.1016/j.physa.2020.124853

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