Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs

Eskandarzade, Neda; Ghorbani, Abozar; Samarfard, Samira; Dı́az, José; Guzzi, Pietro Hiram; Fariborzi, Niloofar; Tahmasebi, Ahmad; Izadpanah, K.

doi:10.1016/j.compbiomed.2022.105575

Cited by 17 publications

(12 citation statements)

References 140 publications

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“…Similar to multiplex networks, temporal networks coupled to the SIR model have been used to model the infection spreading [32] , [33] , [34] . In [35] , a temporal network implemented as a multiplex network with time-varying edges was used to model an epidemic.…”

Section: Related Workmentioning

confidence: 99%

Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading

Petrizzelli

Guzzi²,

Mazza³

2022

Computational and Structural Biotechnology Journal

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Section: Related Workmentioning

confidence: 99%

Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading

Petrizzelli

Guzzi²,

Mazza³

2022

Computational and Structural Biotechnology Journal

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“…The shallow embedding methods do not involve in the continuous learning schema of end-to-end learning framework. However, with an exception, the GNN based embedding methods, as a result of their internal mechanisms of message passing, do posses the essential characteristics for captivating and preserving network's property in the vectorized information and they generally provide a deep multilayered approach towards inferring new/missing link likelihood [57]. In addition to that, we further tend to observe that only a few of the embedding algorithms which encode the graph information actually prove and evaluate their respective embedding by including it for link prediction task.…”

Section: Reports Synopsis Of Various Graph Embedding Techniquesmentioning

confidence: 99%

Empirical Assessment of Graph Embedding Techniques for Predicting Missing Links in Biological Networks

Teji¹,

Dhami²,

Roy³

et al. 2022

Preprint

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Network science tries to shed light into the complex relationships among entities of a system. For instance, biological networks represent relations between macro molecules such as genes, proteins or other small chemicals. Often potential links are guessed computationally due to expensive nature of wet lab experiments. Conventional link prediction techniques consider local network wiring structure, which may not able to infer true relationships. The recent approaches of graph embedding (or representation learning) aims to capture the complete network structure that may be utilized for link prediction. In this work, we assess the performance of ten (10) state-of-the-art embedding techniques for their effectiveness of link prediction in homogeneous and heterogeneous biological networks. Majority of the graph embedding methods, in its original form, not in a position to predict links. We use the latent representation of the network produce by the embedding methods and recreate the network using various similarity and kernal functions. We evaluate nine (09) such functions in combination with candidate embedding methods. We even compare the performance of five (05) traditional, local structure based link prediction methods to show the superiority. Experimental results clearly reveal that Graph Neural Network (GNN) and Attention based encoders with dot product based decoder are the best performers in predicting missing links for both homogeneous and heterogeneous biological network.

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“…Вирусные белки SARS-CoV-2 связываются с несколькими сигнальными белками врождённого иммунитета. На интерфероновый сигнальный каскад нацелены NSP13, NSP15 и ORF9b; на путь NF-κB нацелены NSP13 и ORF9c [34]. Кроме того, две другие убиквитинлигазы E3, которые регулируют противовирусную передачу сигналов врождённого иммунитета (TRIM59 и MIB1) связаны с ORF3a и NSP9 соответственно [26].…”

Section: взаимодействие белков Sars-cov-2 с белками человекаunclassified

Molecular basis of biological activity of polysaccharides in COVID-19 associated conditions

et al. 2022

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The review considers the main molecular biological features of the COVID-19 causative agent, the SARS-CoV-2 virus: life cycle, viral cell penetration strategies, interactions of viral proteins with human proteins, cytopathic effects. We also analyze pathological conditions that occur both during the course of the COVID-19 disease and after virus elimination. A brief review of the biological activities of polysaccharides isolated from various sources is given, and possible molecular biological mechanisms of these activities are considered. Data analysis shows that polysaccharides are a class of biological molecules with wide potential for use in the treatment of both acute conditions in COVID-19 and post-COVID syndrome.

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Network for network concept offers new insights into host- SARS-CoV-2 protein interactions and potential novel targets for developing antiviral drugs

Cited by 17 publications

References 140 publications

Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading

Beyond COVID-19 pandemic: Topology-aware optimization of vaccination strategy for minimizing virus spreading

Empirical Assessment of Graph Embedding Techniques for Predicting Missing Links in Biological Networks

Molecular basis of biological activity of polysaccharides in COVID-19 associated conditions

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