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A speedup technique for (l, d)-motif finding algorithms

Overview of attention for article published in BMC Research Notes, March 2011
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Mentioned by

wikipedia
2 Wikipedia pages

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mendeley
21 Mendeley
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1 CiteULike
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Title
A speedup technique for (l, d)-motif finding algorithms
Published in
BMC Research Notes, March 2011
DOI 10.1186/1756-0500-4-54
Pubmed ID
Authors

Sanguthevar Rajasekaran, Hieu Dinh

Abstract

The discovery of patterns in DNA, RNA, and protein sequences has led to the solution of many vital biological problems. For instance, the identification of patterns in nucleic acid sequences has resulted in the determination of open reading frames, identification of promoter elements of genes, identification of intron/exon splicing sites, identification of SH RNAs, location of RNA degradation signals, identification of alternative splicing sites, etc. In protein sequences, patterns have proven to be extremely helpful in domain identification, location of protease cleavage sites, identification of signal peptides, protein interactions, determination of protein degradation elements, identification of protein trafficking elements, etc. Motifs are important patterns that are helpful in finding transcriptional regulatory elements, transcription factor binding sites, functional genomics, drug design, etc. As a result, numerous papers have been written to solve the motif search problem.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iraq 1 5%
India 1 5%
Unknown 19 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 29%
Student > Doctoral Student 3 14%
Student > Postgraduate 3 14%
Student > Master 2 10%
Professor 2 10%
Other 3 14%
Unknown 2 10%
Readers by discipline Count As %
Computer Science 14 67%
Biochemistry, Genetics and Molecular Biology 3 14%
Agricultural and Biological Sciences 1 5%
Unknown 3 14%
Attention Score in Context

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 21 July 2019.
All research outputs
#7,454,951
of 22,790,780 outputs
Outputs from BMC Research Notes
#1,240
of 4,262 outputs
Outputs of similar age
#39,263
of 108,540 outputs
Outputs of similar age from BMC Research Notes
#17
of 34 outputs
Altmetric has tracked 22,790,780 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,262 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 65% 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 108,540 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.