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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders
|
|---|---|
| Published in |
Frontiers in Pharmacology, October 2017
|
| DOI | 10.3389/fphar.2017.00747 |
| Pubmed ID | |
| Authors |
Jiansong Fang, Li Gao, Huili Ma, Qihui Wu, Tian Wu, Jun Wu, Qi Wang, Feixiong Cheng |
| Abstract |
Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 22% |
| United States | 1 | 11% |
| Switzerland | 1 | 11% |
| Unknown | 5 | 56% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 89% |
| Scientists | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 16% |
| Researcher | 6 | 16% |
| Professor > Associate Professor | 4 | 11% |
| Other | 3 | 8% |
| Student > Doctoral Student | 2 | 5% |
| Other | 6 | 16% |
| Unknown | 11 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 18% |
| Pharmacology, Toxicology and Pharmaceutical Science | 5 | 13% |
| Agricultural and Biological Sciences | 3 | 8% |
| Medicine and Dentistry | 2 | 5% |
| Chemistry | 2 | 5% |
| Other | 6 | 16% |
| Unknown | 13 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 November 2017.
All research outputs
#7,500,672
of 23,577,654 outputs
Outputs from Frontiers in Pharmacology
#3,248
of 17,181 outputs
Outputs of similar age
#120,407
of 328,180 outputs
Outputs of similar age from Frontiers in Pharmacology
#56
of 290 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 17,181 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 80% 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 328,180 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 62% of its contemporaries.
We're also able to compare this research output to 290 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.