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Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks

Overview of attention for article published in BMC Medical Genomics, February 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

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12 tweeters

Citations

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14 Dimensions

Readers on

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33 Mendeley
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Title
Novel candidate genes important for asthma and hypertension comorbidity revealed from associative gene networks
Published in
BMC Medical Genomics, February 2018
DOI 10.1186/s12920-018-0331-4
Pubmed ID
Authors

Olga V. Saik, Pavel S. Demenkov, Timofey V. Ivanisenko, Elena Yu Bragina, Maxim B. Freidin, Irina A. Goncharova, Victor E. Dosenko, Olga I. Zolotareva, Ralf Hofestaedt, Inna N. Lavrik, Evgeny I. Rogaev, Vladimir A. Ivanisenko

Abstract

Hypertension and bronchial asthma are a major issue for people's health. As of 2014, approximately one billion adults, or ~ 22% of the world population, have had hypertension. As of 2011, 235-330 million people globally have been affected by asthma and approximately 250,000-345,000 people have died each year from the disease. The development of the effective treatment therapies against these diseases is complicated by their comorbidity features. This is often a major problem in diagnosis and their treatment. Hence, in this study the bioinformatical methodology for the analysis of the comorbidity of these two diseases have been developed. As such, the search for candidate genes related to the comorbid conditions of asthma and hypertension can help in elucidating the molecular mechanisms underlying the comorbid condition of these two diseases, and can also be useful for genotyping and identifying new drug targets. Using ANDSystem, the reconstruction and analysis of gene networks associated with asthma and hypertension was carried out. The gene network of asthma included 755 genes/proteins and 62,603 interactions, while the gene network of hypertension - 713 genes/proteins and 45,479 interactions. Two hundred and five genes/proteins and 9638 interactions were shared between asthma and hypertension. An approach for ranking genes implicated in the comorbid condition of two diseases was proposed. The approach is based on nine criteria for ranking genes by their importance, including standard methods of gene prioritization (Endeavor, ToppGene) as well as original criteria that take into account the characteristics of an associative gene network and the presence of known polymorphisms in the analysed genes. According to the proposed approach, the genes IL10, TLR4, and CAT had the highest priority in the development of comorbidity of these two diseases. Additionally, it was revealed that the list of top genes is enriched with apoptotic genes and genes involved in biological processes related to the functioning of central nervous system. The application of methods of reconstruction and analysis of gene networks is a productive tool for studying the molecular mechanisms of comorbid conditions. The method put forth to rank genes by their importance to the comorbid condition of asthma and hypertension was employed that resulted in prediction of 10 genes, playing the key role in the development of the comorbid condition. The results can be utilised to plan experiments for identification of novel candidate genes along with searching for novel pharmacological targets.

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 15%
Student > Postgraduate 4 12%
Student > Ph. D. Student 4 12%
Unspecified 2 6%
Student > Master 2 6%
Other 4 12%
Unknown 12 36%
Readers by discipline Count As %
Medicine and Dentistry 8 24%
Biochemistry, Genetics and Molecular Biology 6 18%
Computer Science 4 12%
Agricultural and Biological Sciences 1 3%
Environmental Science 1 3%
Other 0 0%
Unknown 13 39%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 May 2020.
All research outputs
#3,788,888
of 15,686,308 outputs
Outputs from BMC Medical Genomics
#182
of 818 outputs
Outputs of similar age
#81,192
of 277,687 outputs
Outputs of similar age from BMC Medical Genomics
#2
of 15 outputs
Altmetric has tracked 15,686,308 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 818 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,687 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.