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Attention Score in Context
| Title |
A polynomial delay algorithm for the enumeration of bubbles with length constraints in directed graphs
|
|---|---|
| Published in |
Algorithms for Molecular Biology, June 2015
|
| DOI | 10.1186/s13015-015-0046-4 |
| Pubmed ID | |
| Authors |
Gustavo Sacomoto, Vincent Lacroix, Marie-France Sagot |
| Abstract |
The problem of enumerating bubbles with length constraints in directed graphs arises in transcriptomics where the question is to identify all alternative splicing events present in a sample of mRNAs sequenced by RNA-seq. We present a new algorithm for enumerating bubbles with length constraints in weighted directed graphs. This is the first polynomial delay algorithm for this problem and we show that in practice, it is faster than previous approaches. This settles one of the main open questions from Sacomoto et al. (BMC Bioinform 13:5, 2012). Moreover, the new algorithm allows us to deal with larger instances and possibly detect longer alternative splicing events. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 20% |
| United Kingdom | 1 | 20% |
| United States | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 80% |
| Members of the public | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 1 | 33% |
| Student > Master | 1 | 33% |
| Unknown | 1 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 67% |
| Unknown | 1 | 33% |
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 06 July 2015.
All research outputs
#13,207,176
of 22,815,414 outputs
Outputs from Algorithms for Molecular Biology
#93
of 264 outputs
Outputs of similar age
#120,099
of 263,249 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 9 outputs
Altmetric has tracked 22,815,414 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 63% 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 263,249 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 53% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.