A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks

Abstract

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts of high-throughput data provide us with opportunities to detect essential proteins from protein interaction networks (PINs). Existing network-based approaches are limited by the poor quality of the underlying PIN data, which exhibits high rates of false positive and false negative results. To overcome this problem, researchers have focused on the prediction of essential proteins by combining PINs with other biological data, which has led to the emergence of various interactions between proteins. It remains challenging, however, to use aggregated multiplex interactions within a single analysis framework to identify essential proteins. In this study, we created a multiplex biological network (MON) by initially integrating PINs, protein domains, and gene expression profiles. Next, we proposed a new approach to discover essential proteins by extending the random walk with restart algorithm to the tensor, which provides a data model representation of the MON. In contrast to existing approaches, the proposed MON approach considers for the importance of nodes and the different types of interactions between proteins during the iteration. MON was implemented to identify essential proteins within two yeast PINs. Our comprehensive experimental results demonstrated that MON outperformed 11 other state-of-the-art approaches in terms of precision-recall curve, jackknife curve, and other criteria.

Complex Network Analysis of Mass Violation, Specifically Mass Killing

“…Various terminologies related to complex networks are described [ 19 ]. Network analysis has been used in many real-world phenomena on various networks, e.g., on US migration networks data [ 7 ], conflicting data [ 4 , 5 ], human life/interactions [ 20 , 21 ], railway express network [ 22 ], ecological networks [ 23 , 24 ], social media network data [ 25 , 26 ], and biological network data [ 27 , 28 , 29 ]. Analyzing a network through multiple approaches such as quantitative and qualitative ones can provide a much more detailed explanation, deeper insight, and better understanding of the development of the network, network dynamics, and growth pattern of nodes.…”

“…Power laws are well-known all through nature, in areas such as astronomy, etymology, and neuroscience [ 20 , 27 , 29 , 49 ]. The power-law distribution is hypothetically fascinating due to the heavy tail.…”

A disease-related essential protein prediction model based on the transfer neural network

“…J Luo et al introduced a prediction method ECC (edge clustering coefficient) based on the complex co expression data of proteins and PPI networks ( Luo and Wu, 2015 ). Bihai Zhao et al proposed a model based on multiple biological networks ( Zhao et al, 2020a ) and a model based on diffusion distance network ( Zhao et al, 2020b ) to predict essential proteins respectively. S. Li et al designed an iterative method called CVIM ( Li et al, 2020 ) based on topological and functional characteristics of proteins to predict key proteins.…”

Identification of essential proteins based on edge features and the fusion of multiple-source biological information

“…On the basis of the relations between protein functions and subcellular localization, Li et al presented the SPP method [ 32 ]. Zhao et al presented two methods, DSN and MON, by integrating PINs, protein domains, gene expression profiles, orthologous proteins, and subcellular localization information [ 33 , 34 ].…”