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Mendeley readers
Attention Score in Context
Title |
Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences
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Published in |
BMC Bioinformatics, December 2009
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DOI | 10.1186/1471-2105-10-419 |
Pubmed ID | |
Authors |
Yungki Park |
Abstract |
Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward--unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research. |
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 % |
---|---|---|
Japan | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 6% |
Canada | 3 | 4% |
Germany | 2 | 3% |
United States | 2 | 3% |
Japan | 1 | 1% |
India | 1 | 1% |
Unknown | 57 | 81% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 20 | 29% |
Student > Ph. D. Student | 14 | 20% |
Student > Master | 10 | 14% |
Professor > Associate Professor | 7 | 10% |
Professor | 5 | 7% |
Other | 12 | 17% |
Unknown | 2 | 3% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 33 | 47% |
Biochemistry, Genetics and Molecular Biology | 14 | 20% |
Computer Science | 11 | 16% |
Engineering | 5 | 7% |
Business, Management and Accounting | 1 | 1% |
Other | 1 | 1% |
Unknown | 5 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 09 November 2014.
All research outputs
#18,382,900
of 22,769,322 outputs
Outputs from BMC Bioinformatics
#6,307
of 7,273 outputs
Outputs of similar age
#150,895
of 164,451 outputs
Outputs of similar age from BMC Bioinformatics
#48
of 57 outputs
Altmetric has tracked 22,769,322 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,273 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 164,451 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.