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X Demographics
Mendeley readers
Attention Score in Context
Title |
iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations
|
---|---|
Published in |
Genome Biology, October 2022
|
DOI | 10.1186/s13059-022-02780-1 |
Pubmed ID | |
Authors |
Junru Jin, Yingying Yu, Ruheng Wang, Xin Zeng, Chao Pang, Yi Jiang, Zhongshen Li, Yutong Dai, Ran Su, Quan Zou, Kenta Nakai, Leyi Wei |
X Demographics
The data shown below were collected from the profiles of 32 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 | 8 | 25% |
France | 3 | 9% |
United Kingdom | 3 | 9% |
Sweden | 1 | 3% |
Pakistan | 1 | 3% |
Venezuela, Bolivarian Republic of | 1 | 3% |
China | 1 | 3% |
Korea, Republic of | 1 | 3% |
Italy | 1 | 3% |
Other | 1 | 3% |
Unknown | 11 | 34% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 21 | 66% |
Scientists | 10 | 31% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 31 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 6 | 19% |
Student > Ph. D. Student | 5 | 16% |
Unspecified | 3 | 10% |
Researcher | 3 | 10% |
Student > Bachelor | 1 | 3% |
Other | 5 | 16% |
Unknown | 8 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 5 | 16% |
Computer Science | 4 | 13% |
Unspecified | 3 | 10% |
Agricultural and Biological Sciences | 3 | 10% |
Engineering | 2 | 6% |
Other | 3 | 10% |
Unknown | 11 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 28 October 2022.
All research outputs
#2,401,083
of 25,392,582 outputs
Outputs from Genome Biology
#1,952
of 4,470 outputs
Outputs of similar age
#49,819
of 441,666 outputs
Outputs of similar age from Genome Biology
#31
of 60 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th 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 56% 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 441,666 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 88% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.