| Title |
A network-based meta-analysis for characterizing the genetic landscape of human aging
|
|---|---|
| Published in |
Biogerontology, December 2017
|
| DOI | 10.1007/s10522-017-9741-5 |
| Pubmed ID | |
| Authors |
Hagen Blankenburg, Peter P. Pramstaller, Francisco S. Domingues |
| Abstract |
Great amounts of omics data are generated in aging research, but their diverse and partly complementary nature requires integrative analysis approaches for investigating aging processes and connections to age-related diseases. To establish a broader picture of the genetic and epigenetic landscape of human aging we performed a large-scale meta-analysis of 6600 human genes by combining 35 datasets that cover aging hallmarks, longevity, changes in DNA methylation and gene expression, and different age-related diseases. To identify biological relationships between aging-associated genes we incorporated them into a protein interaction network and characterized their network neighborhoods. In particular, we computed a comprehensive landscape of more than 1000 human aging clusters, network regions where genes are highly connected and where gene products commonly participate in similar processes. In addition to clusters that capture known aging processes such as nutrient-sensing and mTOR signaling, we present a number of clusters with a putative functional role in linking different aging processes as promising candidates for follow-up studies. To enable their detailed exploration, all datasets and aging clusters are made freely available via an interactive website ( https://gemex.eurac.edu/bioinf/age/ ). |
X Demographics
The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 19% |
| France | 2 | 13% |
| United Kingdom | 2 | 13% |
| Switzerland | 1 | 6% |
| Canada | 1 | 6% |
| Spain | 1 | 6% |
| Germany | 1 | 6% |
| Unknown | 5 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 56% |
| Scientists | 6 | 38% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 42 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 26% |
| Researcher | 6 | 14% |
| Other | 5 | 12% |
| Student > Master | 4 | 10% |
| Student > Bachelor | 3 | 7% |
| Other | 7 | 17% |
| Unknown | 6 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 18 | 43% |
| Agricultural and Biological Sciences | 6 | 14% |
| Nursing and Health Professions | 2 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Computer Science | 1 | 2% |
| Other | 5 | 12% |
| Unknown | 9 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 07 January 2018.
All research outputs
#3,366,315
of 25,388,837 outputs
Outputs from Biogerontology
#145
of 719 outputs
Outputs of similar age
#70,477
of 454,322 outputs
Outputs of similar age from Biogerontology
#4
of 9 outputs
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