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Mendeley readers
Attention Score in Context
Chapter title |
The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models
|
---|---|
Chapter number | 4 |
Book title |
Metabolic Network Reconstruction and Modeling
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7528-0_4 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7527-3, 978-1-4939-7528-0
|
Authors |
Maria Pires Pacheco, Thomas Sauter |
Abstract |
The FASTCORE family is a family of algorithms that are mainly used to build context-specific models but can also be applied to other tasks such as gapfilling and consistency testing. The FASTCORE family has very low computational demands with running times that are several orders of magnitude lower than its main competitors. Furthermore, the models built by the FASTCORE family have a better resolution power (defined as the ability to capture metabolic variations between different tissues, cell types, or contexts) than models from other algorithms. |
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 % |
---|---|---|
Italy | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 39 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 9 | 23% |
Student > Master | 6 | 15% |
Researcher | 4 | 10% |
Student > Bachelor | 3 | 8% |
Professor > Associate Professor | 2 | 5% |
Other | 5 | 13% |
Unknown | 10 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 14 | 36% |
Agricultural and Biological Sciences | 10 | 26% |
Computer Science | 4 | 10% |
Unspecified | 1 | 3% |
Immunology and Microbiology | 1 | 3% |
Other | 1 | 3% |
Unknown | 8 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 19 December 2017.
All research outputs
#14,369,953
of 23,011,300 outputs
Outputs from Methods in molecular biology
#4,225
of 13,157 outputs
Outputs of similar age
#240,454
of 442,319 outputs
Outputs of similar age from Methods in molecular biology
#432
of 1,498 outputs
Altmetric has tracked 23,011,300 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,157 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 64% 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 442,319 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,498 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 66% of its contemporaries.