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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MEGADOCK 3.0: a high-performance protein-protein interaction prediction software using hybrid parallel computing for petascale supercomputing environments
|
|---|---|
| Published in |
Source Code for Biology and Medicine, September 2013
|
| DOI | 10.1186/1751-0473-8-18 |
| Pubmed ID | |
| Authors |
Yuri Matsuzaki, Nobuyuki Uchikoga, Masahito Ohue, Takehiro Shimoda, Toshiyuki Sato, Takashi Ishida, Yutaka Akiyama |
| Abstract |
Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures. |
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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 2% |
| Romania | 1 | 2% |
| Spain | 1 | 2% |
| Japan | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 46 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 31% |
| Researcher | 10 | 20% |
| Student > Master | 8 | 16% |
| Student > Bachelor | 3 | 6% |
| Other | 3 | 6% |
| Other | 8 | 16% |
| Unknown | 3 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 45% |
| Computer Science | 9 | 18% |
| Biochemistry, Genetics and Molecular Biology | 8 | 16% |
| Engineering | 3 | 6% |
| Chemistry | 2 | 4% |
| Other | 2 | 4% |
| Unknown | 4 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 05 November 2015.
All research outputs
#6,928,728
of 22,719,618 outputs
Outputs from Source Code for Biology and Medicine
#40
of 127 outputs
Outputs of similar age
#60,023
of 196,897 outputs
Outputs of similar age from Source Code for Biology and Medicine
#3
of 6 outputs
Altmetric has tracked 22,719,618 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 127 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 68% 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 196,897 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 68% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.