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Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach

Overview of attention for article published in Frontiers in Cardiovascular Medicine, July 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 (85th percentile)

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Title
Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach
Published in
Frontiers in Cardiovascular Medicine, July 2018
DOI 10.3389/fcvm.2018.00089
Pubmed ID
Authors

Baiba Vilne, Heribert Schunkert

Abstract

Coronary artery disease (CAD) and myocardial infarction (MI) remain among the leading causes of mortality worldwide, urgently demanding a better understanding of disease etiology, and more efficient therapeutic strategies. Genetic predisposition as well as the environment and lifestyle are thought to contribute to disease risk. It is likely that non-linear and complex interactions occur between these multiple factors, involving simultaneous pathological changes in diverse cell types, tissues, and organs, at multiple molecular levels. Recent technological advances have exponentially expanded the breadth of available -omics data, from genome, epigenome, transcriptome, proteome, metabolome to even the microbiome. Integration of multiple layers of information across several -omics domains, i.e., the so-called multi-omics approach, currently holds the promise as a path toward precision medicine. Indeed, a more meaningful interpretation of genotype-phenotype relationships and the development of successful therapeutics tailored to individual patients are urgently needed. In this review, we will summarize recent findings and applications of integrative multi-omics in elucidating the etiology of CAD/MI; with a special focus on established disease susceptibility loci sequentially identified in genome-wide association studies (GWAS) over the last 10 years. Moreover, in addition to the autosomal genome, we will also consider the genetic variation in our "second genome"-the mitochondrial genome. Finally, we will summarize the current challenges in the field and point to future research directions required in order to successfully and effectively apply these approaches for precision medicine.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 79 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 18%
Student > Master 11 14%
Student > Ph. D. Student 9 11%
Student > Bachelor 8 10%
Student > Doctoral Student 6 8%
Other 15 19%
Unknown 16 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 22%
Medicine and Dentistry 11 14%
Agricultural and Biological Sciences 7 9%
Mathematics 4 5%
Immunology and Microbiology 3 4%
Other 16 20%
Unknown 21 27%
Attention Score in Context

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 02 August 2018.
All research outputs
#6,258,286
of 25,262,379 outputs
Outputs from Frontiers in Cardiovascular Medicine
#1,029
of 9,086 outputs
Outputs of similar age
#88,810
of 302,549 outputs
Outputs of similar age from Frontiers in Cardiovascular Medicine
#10
of 62 outputs
Altmetric has tracked 25,262,379 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 9,086 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 88% 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 302,549 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 62 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.