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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Automated extraction and semantic analysis of mutation impacts from the biomedical literature
|
|---|---|
| Published in |
BMC Genomics, June 2012
|
| DOI | 10.1186/1471-2164-13-s4-s10 |
| Pubmed ID | |
| Authors |
Nona Naderi, René Witte |
| Abstract |
Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually curating the rich and fast growing repository of biomedical literature is expensive and time-consuming. As a solution, text mining approaches have increasingly been deployed in the biomedical domain. While the detection of single-point mutations is well covered by existing systems, challenges still exist in grounding impacts to their respective mutations and recognizing the affected protein properties, in particular kinetic and stability properties together with physical quantities. |
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 % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 8% |
| Japan | 2 | 4% |
| Finland | 1 | 2% |
| Australia | 1 | 2% |
| India | 1 | 2% |
| Unknown | 44 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 25% |
| Student > Ph. D. Student | 8 | 15% |
| Student > Master | 8 | 15% |
| Student > Bachelor | 6 | 11% |
| Student > Doctoral Student | 3 | 6% |
| Other | 8 | 15% |
| Unknown | 7 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 15 | 28% |
| Agricultural and Biological Sciences | 12 | 23% |
| Biochemistry, Genetics and Molecular Biology | 6 | 11% |
| Medicine and Dentistry | 4 | 8% |
| Engineering | 2 | 4% |
| Other | 5 | 9% |
| Unknown | 9 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 08 November 2012.
All research outputs
#14,599,159
of 25,371,288 outputs
Outputs from BMC Genomics
#4,932
of 11,244 outputs
Outputs of similar age
#99,206
of 177,866 outputs
Outputs of similar age from BMC Genomics
#58
of 159 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 54% 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 177,866 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.