scholarly journals HPID: The Human Protein Interaction Database

2004 ◽  
Vol 20 (15) ◽  
pp. 2466-2470 ◽  
Author(s):  
K. Han ◽  
B. Park ◽  
H. Kim ◽  
J. Hong ◽  
J. Park
Keyword(s):  
Protein Interaction ◽  
Human Protein ◽  
Human Protein Interaction ◽  
Interaction Database ◽  
Protein Interaction Database

scholarly journals Towards semi-automated curation: using text mining to recreate the HIV-1, human protein interaction database

Database ◽  
2012 ◽  
Vol 2012 ◽  
Author(s):  
Daniel G. Jamieson ◽  
Martin Gerner ◽  
Farzaneh Sarafraz ◽  
Goran Nenadic ◽  
David L. Robertson
Keyword(s):  
Text Mining ◽  
Protein Interaction ◽  
Human Protein ◽  
Human Protein Interaction ◽  
Interaction Database ◽  
Protein Interaction Database ◽  
Hiv 1

scholarly journals Human immunodeficiency virus type 1, human protein interaction database at NCBI

2009 ◽  
Vol 37 (Database) ◽  
pp. D417-D422 ◽  
Author(s):  
W. Fu ◽  
B. E. Sanders-Beer ◽  
K. S. Katz ◽  
D. R. Maglott ◽  
K. D. Pruitt ◽  
...  
Keyword(s):  
Human Immunodeficiency Virus ◽  
Human Immunodeficiency Virus Type ◽  
Virus Type ◽  
Protein Interaction ◽  
Human Protein ◽  
Human Protein Interaction ◽  
Interaction Database ◽  
Immunodeficiency Virus ◽  
Protein Interaction Database

scholarly journals An inverse small molecule screen to design a chemically defined medium supporting long-term growth of Drosophila cell lines

2014 ◽  
Vol 10 (10) ◽  
pp. 2713-2723 ◽  
Author(s):  
M. Burnette ◽  
T. Brito-Robinson ◽  
J. Li ◽  
J. Zartman
Keyword(s):  
Protein Interaction ◽  
Defined Medium ◽  
Chemically Defined Medium ◽  
Drosophila Cell ◽  
Interaction Database ◽  
Novel Approach ◽  
Small Molecule Screen ◽  
Growth Promoting ◽  
Protein Interaction Database

We describe a novel approach to screen for growth promoting compounds and score putative targets using a drug–protein interaction database.

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.

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