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 |
DEEPSEN: a convolutional neural network based method for super-enhancer prediction
|
|---|---|
| Published in |
BMC Bioinformatics, December 2019
|
| DOI | 10.1186/s12859-019-3180-z |
| Pubmed ID | |
| Authors |
Hongda Bu, Jiaqi Hao, Yanglan Gan, Shuigeng Zhou, Jihong Guan |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 10% |
| Nigeria | 1 | 10% |
| Brazil | 1 | 10% |
| Switzerland | 1 | 10% |
| India | 1 | 10% |
| France | 1 | 10% |
| United Kingdom | 1 | 10% |
| Unknown | 3 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 90% |
| Scientists | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 17% |
| Student > Ph. D. Student | 4 | 13% |
| Student > Postgraduate | 4 | 13% |
| Student > Master | 3 | 10% |
| Student > Bachelor | 2 | 7% |
| Other | 4 | 13% |
| Unknown | 8 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 9 | 30% |
| Computer Science | 6 | 20% |
| Agricultural and Biological Sciences | 2 | 7% |
| Business, Management and Accounting | 1 | 3% |
| Arts and Humanities | 1 | 3% |
| Other | 2 | 7% |
| Unknown | 9 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 29 December 2019.
All research outputs
#6,687,473
of 24,133,587 outputs
Outputs from BMC Bioinformatics
#2,458
of 7,504 outputs
Outputs of similar age
#138,893
of 464,841 outputs
Outputs of similar age from BMC Bioinformatics
#61
of 211 outputs
Altmetric has tracked 24,133,587 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,504 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 67% 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 464,841 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 69% of its contemporaries.
We're also able to compare this research output to 211 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 71% of its contemporaries.