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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Efficient sequential and parallel algorithms for planted motif search
|
|---|---|
| Published in |
BMC Bioinformatics, January 2014
|
| DOI | 10.1186/1471-2105-15-34 |
| Pubmed ID | |
| Authors |
Marius Nicolae, Sanguthevar Rajasekaran |
| Abstract |
Motif searching is an important step in the detection of rare events occurring in a set of DNA or protein sequences. One formulation of the problem is known as (l,d)-motif search or Planted Motif Search (PMS). In PMS we are given two integers l and d and n biological sequences. We want to find all sequences of length l that appear in each of the input sequences with at most d mismatches. The PMS problem is NP-complete. PMS algorithms are typically evaluated on certain instances considered challenging. Despite ample research in the area, a considerable performance gap exists because many state of the art algorithms have large runtimes even for moderately challenging instances. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 6% |
| Iraq | 1 | 3% |
| India | 1 | 3% |
| China | 1 | 3% |
| Unknown | 27 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 25% |
| Student > Master | 5 | 16% |
| Student > Doctoral Student | 3 | 9% |
| Student > Bachelor | 3 | 9% |
| Other | 2 | 6% |
| Other | 8 | 25% |
| Unknown | 3 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 56% |
| Agricultural and Biological Sciences | 4 | 13% |
| Engineering | 3 | 9% |
| Nursing and Health Professions | 1 | 3% |
| Mathematics | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 3 | 9% |
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 09 February 2014.
All research outputs
#14,773,697
of 22,743,667 outputs
Outputs from BMC Bioinformatics
#5,040
of 7,268 outputs
Outputs of similar age
#182,383
of 306,968 outputs
Outputs of similar age from BMC Bioinformatics
#60
of 95 outputs
Altmetric has tracked 22,743,667 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,268 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 26th percentile – i.e., 26% 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 306,968 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 95 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.