You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
A framework for application of metabolic modeling in yeast to predict the effects of nsSNV in human orthologs
|
|---|---|
| Published in |
Biology Direct, June 2014
|
| DOI | 10.1186/1745-6150-9-9 |
| Pubmed ID | |
| Authors |
Hayley Dingerdissen, Daniel S Weaver, Peter D Karp, Yang Pan, Vahan Simonyan, Raja Mazumder |
| Abstract |
We have previously suggested a method for proteome wide analysis of variation at functional residues wherein we identified the set of all human genes with nonsynonymous single nucleotide variation (nsSNV) in the active site residue of the corresponding proteins. 34 of these proteins were shown to have a 1:1:1 enzyme:pathway:reaction relationship, making these proteins ideal candidates for laboratory validation through creation and observation of specific yeast active site knock-outs and downstream targeted metabolomics experiments. Here we present the next step in the workflow toward using yeast metabolic modeling to predict human metabolic behavior resulting from nsSNV. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 24% |
| Student > Ph. D. Student | 4 | 16% |
| Student > Doctoral Student | 3 | 12% |
| Student > Bachelor | 2 | 8% |
| Professor | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 6 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 32% |
| Biochemistry, Genetics and Molecular Biology | 4 | 16% |
| Engineering | 3 | 12% |
| Medicine and Dentistry | 2 | 8% |
| Computer Science | 1 | 4% |
| Other | 3 | 12% |
| Unknown | 4 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 26 August 2014.
All research outputs
#17,722,094
of 22,757,090 outputs
Outputs from Biology Direct
#399
of 487 outputs
Outputs of similar age
#155,746
of 227,901 outputs
Outputs of similar age from Biology Direct
#5
of 8 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 487 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
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 227,901 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.