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X Demographics
Mendeley readers
Attention Score in Context
| Title |
BUFET: boosting the unbiased miRNA functional enrichment analysis using bitsets
|
|---|---|
| Published in |
BMC Bioinformatics, September 2017
|
| DOI | 10.1186/s12859-017-1812-8 |
| Pubmed ID | |
| Authors |
Konstantinos Zagganas, Thanasis Vergoulis, Maria D. Paraskevopoulou, Ioannis S. Vlachos, Spiros Skiadopoulos, Theodore Dalamagas |
| Abstract |
A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological processes that may be regulated by a given miRNA group. However, it is computationally intensive and significantly more expensive than its alternatives. We introduce BUFET, a new approach to significantly reduce the time required for the execution of the unbiased miRNA functional enrichment analysis. It derives its strength from the utilization of efficient bitset-based methods and parallel computation techniques. BUFET outperforms the state-of-the-art implementation, in regard to computational efficiency, in all scenarios (both single- and multi-core), being, in some cases, more than one order of magnitude faster. |
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 % |
|---|---|---|
| Greece | 2 | 29% |
| Unknown | 5 | 71% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 38% |
| Student > Ph. D. Student | 3 | 23% |
| Student > Master | 1 | 8% |
| Lecturer | 1 | 8% |
| Professor > Associate Professor | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 1 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 31% |
| Agricultural and Biological Sciences | 2 | 15% |
| Engineering | 2 | 15% |
| Computer Science | 1 | 8% |
| Mathematics | 1 | 8% |
| Other | 2 | 15% |
| Unknown | 1 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 25 April 2019.
All research outputs
#4,051,056
of 23,001,641 outputs
Outputs from BMC Bioinformatics
#1,546
of 7,312 outputs
Outputs of similar age
#71,641
of 315,600 outputs
Outputs of similar age from BMC Bioinformatics
#21
of 101 outputs
Altmetric has tracked 23,001,641 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 78% 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 315,600 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 77% of its contemporaries.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.