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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences
|
|---|---|
| Published in |
Genome Biology, August 2021
|
| DOI | 10.1186/s13059-021-02453-5 |
| Pubmed ID | |
| Authors |
Fan Cao, Yu Zhang, Yichao Cai, Sambhavi Animesh, Ying Zhang, Semih Can Akincilar, Yan Ping Loh, Xinya Li, Wee Joo Chng, Vinay Tergaonkar, Chee Keong Kwoh, Melissa J. Fullwood |
X Demographics
The data shown below were collected from the profiles of 27 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 States | 7 | 26% |
| Singapore | 2 | 7% |
| Russia | 1 | 4% |
| India | 1 | 4% |
| Brazil | 1 | 4% |
| Egypt | 1 | 4% |
| United Kingdom | 1 | 4% |
| Unknown | 13 | 48% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 18 | 67% |
| Scientists | 8 | 30% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 49 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 20% |
| Researcher | 8 | 16% |
| Student > Master | 6 | 12% |
| Professor > Associate Professor | 3 | 6% |
| Professor | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 15 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 15 | 31% |
| Computer Science | 7 | 14% |
| Agricultural and Biological Sciences | 5 | 10% |
| Engineering | 2 | 4% |
| Social Sciences | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 15 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 21 January 2022.
All research outputs
#2,267,080
of 25,399,318 outputs
Outputs from Genome Biology
#1,871
of 4,470 outputs
Outputs of similar age
#51,026
of 421,352 outputs
Outputs of similar age from Genome Biology
#46
of 81 outputs
Altmetric has tracked 25,399,318 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 58% 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 421,352 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 81 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.