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Novel methods to optimize gene and statistic test for evaluation – an application for Escherichia coli

Overview of attention for article published in BMC Bioinformatics, February 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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1 X user
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2 patents

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4 Dimensions

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29 Mendeley
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Title
Novel methods to optimize gene and statistic test for evaluation – an application for Escherichia coli
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1517-z
Pubmed ID
Authors

Tran Tuan-Anh, Le Thi Ly, Ngo Quoc Viet, Pham The Bao

Abstract

Since the recombinant protein was discovered, it has become more popular in many aspects of life science. The value of global pharmaceutical market was $87 billion in 2008 and the sales for industrial enzyme exceeded $4 billion in 2012. This is strong evidence showing the great potential of recombinant protein. However, native genes introduced into a host can cause incompatibility of codon usage bias, GC content, repeat region, Shine-Dalgarno sequence with host's expression system, so the yields can fall down significantly. Hence, we propose novel methods for gene optimization based on neural network, Bayesian theory, and Euclidian distance. The correlation coefficients of our neural network are 0.86, 0.73, and 0.90 in training, validation, and testing process. In addition, genes optimized by our methods seem to associate with highly expressed genes and give reasonable codon adaptation index values. Furthermore, genes optimized by the proposed methods are highly matched with the previous experimental data. The proposed methods have high potential for gene optimization and further researches in gene expression. We built a demonstrative program using Matlab R2014a under Mac OS X. The program was published in both standalone executable program and Matlab function files. The developed program can be accessed from http://www.math.hcmus.edu.vn/~ptbao/paper_soft/GeneOptProg/ .

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 17%
Researcher 5 17%
Student > Master 4 14%
Student > Bachelor 2 7%
Student > Doctoral Student 2 7%
Other 2 7%
Unknown 9 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 17%
Computer Science 5 17%
Agricultural and Biological Sciences 3 10%
Psychology 2 7%
Engineering 2 7%
Other 2 7%
Unknown 10 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 January 2024.
All research outputs
#5,217,644
of 25,366,663 outputs
Outputs from BMC Bioinformatics
#1,821
of 7,677 outputs
Outputs of similar age
#100,525
of 434,998 outputs
Outputs of similar age from BMC Bioinformatics
#36
of 149 outputs
Altmetric has tracked 25,366,663 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,677 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 done well, scoring higher than 75% 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 434,998 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 76% of its contemporaries.
We're also able to compare this research output to 149 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.