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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling
|
|---|---|
| Published in |
BMC Bioinformatics, November 2022
|
| DOI | 10.1186/s12859-022-05009-x |
| Pubmed ID | |
| Authors |
Kazuhiro Maeda, Aoi Hatae, Yukie Sakai, Fred C. Boogerd, Hiroyuki Kurata |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 14% |
| Sweden | 1 | 14% |
| United Kingdom | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 57% |
| Members of the public | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 25% |
| Researcher | 2 | 13% |
| Lecturer > Senior Lecturer | 1 | 6% |
| Student > Master | 1 | 6% |
| Student > Postgraduate | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 7 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 19% |
| Chemical Engineering | 2 | 13% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Immunology and Microbiology | 1 | 6% |
| Medicine and Dentistry | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 8 | 50% |
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 15 November 2022.
All research outputs
#14,557,947
of 24,820,264 outputs
Outputs from BMC Bioinformatics
#4,183
of 7,593 outputs
Outputs of similar age
#180,918
of 435,190 outputs
Outputs of similar age from BMC Bioinformatics
#73
of 169 outputs
Altmetric has tracked 24,820,264 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,593 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 42nd percentile – i.e., 42% 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 435,190 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 57% of its contemporaries.
We're also able to compare this research output to 169 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 52% of its contemporaries.