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Sequence motif finder using memetic algorithm

Overview of attention for article published in BMC Bioinformatics, January 2018
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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4 tweeters
facebook
1 Facebook page
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1 research highlight platform

Citations

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7 Dimensions

Readers on

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35 Mendeley
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1 CiteULike
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Title
Sequence motif finder using memetic algorithm
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-017-2005-1
Pubmed ID
Authors

Jader M. Caldonazzo Garbelini, André Y. Kashiwabara, Danilo S. Sanches

Abstract

De novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it is an important problem in Bioinformatics. The correct recognition of TFBS plays an important role in understanding the mechanisms of gene regulation and helps to develop new drugs. We here present Memetic Framework for Motif Discovery (MFMD), an algorithm that uses semi-greedy constructive heuristics as a local optimizer. In addition, we used a hybridization of the classic genetic algorithm as a global optimizer to refine the solutions initially found. MFMD can find and classify overrepresented patterns in DNA sequences and predict their respective initial positions. MFMD performance was assessed using ChIP-seq data retrieved from the JASPAR site, promoter sequences extracted from the ABS site, and artificially generated synthetic data. The MFMD was evaluated and compared with well-known approaches in the literature, called MEME and Gibbs Motif Sampler, achieving a higher f-score in the most datasets used in this work. We have developed an approach for detecting motifs in biopolymers sequences. MFMD is a freely available software that can be promising as an alternative to the development of new tools for de novo motif discovery. Its open-source software can be downloaded at https://github.com/jadermcg/mfmd .

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 29%
Student > Bachelor 6 17%
Researcher 5 14%
Student > Ph. D. Student 5 14%
Student > Doctoral Student 2 6%
Other 3 9%
Unknown 4 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 43%
Computer Science 12 34%
Agricultural and Biological Sciences 2 6%
Engineering 2 6%
Nursing and Health Professions 1 3%
Other 0 0%
Unknown 3 9%

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 15 April 2019.
All research outputs
#4,959,920
of 16,639,069 outputs
Outputs from BMC Bioinformatics
#2,105
of 5,985 outputs
Outputs of similar age
#138,168
of 413,137 outputs
Outputs of similar age from BMC Bioinformatics
#151
of 446 outputs
Altmetric has tracked 16,639,069 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 5,985 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. 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 413,137 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 66% of its contemporaries.
We're also able to compare this research output to 446 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.