Mining Quasi-Bicliques from HIV-1-Human Protein Interaction Network: A Multiobjective Biclustering Approach

2013 ◽  
Vol 10 (2) ◽  
pp. 423-435 ◽  
Author(s):  
Ujjwal Maulik ◽  
Anirban Mukhopadhyay ◽  
Malay Bhattacharyya ◽  
Lars Kaderali ◽  
Benedikt Brors ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Human Protein ◽  
Human Protein Interaction ◽  
Hiv 1

scholarly journals Identification of COVID-19 Infection-Related Human Genes Based on a Random Walk Model in a Virus–Human Protein Interaction Network

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
YuHang Zhang ◽  
Tao Zeng ◽  
Lei Chen ◽  
ShiJian Ding ◽  
Tao Huang ◽  
...  
Keyword(s):  
Random Walk ◽  
Infectious Diseases ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Disease Outbreaks ◽  
Interaction Network ◽  
Random Walk Model ◽  
Human Protein ◽  
Human Beings ◽  
Human Protein Interaction

Coronaviruses are specific crown-shaped viruses that were first identified in the 1960s, and three typical examples of the most recent coronavirus disease outbreaks include severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19. Particularly, COVID-19 is currently causing a worldwide pandemic, threatening the health of human beings globally. The identification of viral pathogenic mechanisms is important for further developing effective drugs and targeted clinical treatment methods. The delayed revelation of viral infectious mechanisms is currently one of the technical obstacles in the prevention and treatment of infectious diseases. In this study, we proposed a random walk model to identify the potential pathological mechanisms of COVID-19 on a virus–human protein interaction network, and we effectively identified a group of proteins that have already been determined to be potentially important for COVID-19 infection and for similar SARS infections, which help further developing drugs and targeted therapeutic methods against COVID-19. Moreover, we constructed a standard computational workflow for predicting the pathological biomarkers and related pharmacological targets of infectious diseases.

scholarly journals Identification ofInfluenza A/H7N9 Virus Infection-Related Human Genes Based on Shortest Paths in a Virus-Human Protein Interaction Network

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ning Zhang ◽  
Min Jiang ◽  
Tao Huang ◽  
Yu-Dong Cai
Keyword(s):  
Virus Infection ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Influenza A ◽  
Shortest Paths ◽  
Interaction Network ◽  
Human Protein ◽  
H7n9 Virus ◽  
Human Protein Interaction ◽  
Human Genes

The recently emergingInfluenza A/H7N9 virus is reported to be able to infect humans and cause mortality. However, viral and host factors associated with the infection are poorly understood. It is suggested by the “guilt by association” rule that interacting proteins share the same or similar functions and hence may be involved in the same pathway. In this study, we developed a computational method to identifyInfluenza A/H7N9 virus infection-related human genes based on this rule from the shortest paths in a virus-human protein interaction network. Finally, we screened out the most significant 20 human genes, which could be the potential infection related genes, providing guidelines for further experimental validation. Analysis of the 20 genes showed that they were enriched in protein binding, saccharide or polysaccharide metabolism related pathways and oxidative phosphorylation pathways. We also compared the results with those from human rhinovirus (HRV) and respiratory syncytial virus (RSV) by the same method. It was indicated that saccharide or polysaccharide metabolism related pathways might be especially associated with the H7N9 infection. These results could shed some light on the understanding of the virus infection mechanism, providing basis for future experimental biology studies and for the development of effective strategies for H7N9 clinical therapies.

scholarly journals Short Communication: Cataloguing the HIV Type 1 Human Protein Interaction Network

2008 ◽  
Vol 24 (12) ◽  
pp. 1497-1502 ◽  
Author(s):  
Roger G. Ptak ◽  
William Fu ◽  
Brigitte E. Sanders-Beer ◽  
Jonathan E. Dickerson ◽  
John W. Pinney ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Short Communication ◽  
Interaction Network ◽  
Human Protein ◽  
Human Protein Interaction ◽  
Hiv Type 1

scholarly journals The latent geometry of the human protein interaction network

2017 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Pablo Mier ◽  
Miguel A. Andrade-Navarro
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Human Protein ◽  
Biologically Relevant ◽  
Human Protein Interaction ◽  
Connection Probability ◽  
Cellular Localisation ◽  
Complex Architecture

AbstractTo mine valuable information from the complex architecture of the human protein interaction network (hPIN), we require models able to describe its growth and dynamics accurately. Here, we present evidence that uncovering the latent geometry of the hPIN can ease challenging problems in systems biology. We embedded the hPIN to hyperbolic space, whose geometric properties reflect the characteristic scale invariance and strong clustering of the network. Interestingly, the inferred hyperbolic coordinates of nodes capture biologically relevant features, like protein age, function and cellular localisation. We also realised that the shorter the distance between two proteins in the embedding space, the higher their connection probability, which resulted in the prediction of plausible protein interactions. Finally, we observed that proteins can efficiently communicate with each other via a greedy routeing process, guided by the latent geometry of the hPIN. When analysed from the appropriate biological context, these efficient communication channels can be used to determine the core members of signal transduction pathways and to study how system perturbations impact their efficiency.

scholarly journals The latent geometry of the human protein interaction network

2018 ◽  
Vol 34 (16) ◽  
pp. 2826-2834 ◽  
Author(s):  
Gregorio Alanis-Lobato ◽  
Pablo Mier ◽  
Miguel Andrade-Navarro
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Human Protein ◽  
Human Protein Interaction
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