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X Demographics
Mendeley readers
Attention Score in Context
| Title |
DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels
|
|---|---|
| Published in |
Genome Biology, March 2013
|
| DOI | 10.1186/gb-2013-14-3-r23 |
| Pubmed ID | |
| Authors |
Huiying Zhao, Yuedong Yang, Hai Lin, Xinjun Zhang, Matthew Mort, David N Cooper, Yunlong Liu, Yaoqi Zhou |
| Abstract |
Micro-indels (insertions or deletions shorter than 21 bps) constitute the second most frequent class of human gene mutation after single nucleotide variants. Despite the relative abundance of non-frameshifting indels, their damaging effect on protein structure and function has gone largely unstudied. We have developed a support vector machine-based method named DDIG-in (Detecting disease-causing genetic variations due to indels) to prioritize non-frameshifting indels by comparing disease-associated mutations with putatively neutral mutations from the 1,000 Genomes Project. The final model gives good discrimination for indels and is robust against annotation errors. A webserver implementing DDIG-in is available at http://sparks-lab.org/ddig. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hong Kong | 1 | 2% |
| United States | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 49 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 21% |
| Researcher | 10 | 19% |
| Student > Bachelor | 5 | 10% |
| Student > Master | 5 | 10% |
| Professor | 3 | 6% |
| Other | 10 | 19% |
| Unknown | 8 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 16 | 31% |
| Biochemistry, Genetics and Molecular Biology | 14 | 27% |
| Computer Science | 7 | 13% |
| Medicine and Dentistry | 1 | 2% |
| Chemistry | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 12 | 23% |
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 14 March 2013.
All research outputs
#20,657,128
of 25,374,917 outputs
Outputs from Genome Biology
#4,269
of 4,467 outputs
Outputs of similar age
#160,973
of 208,880 outputs
Outputs of similar age from Genome Biology
#45
of 45 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 1st percentile – i.e., 1% 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 208,880 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.