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The prediction of virus mutation using neural networks and rough set techniques

Overview of attention for article published in EURASIP Journal on Bioinformatics & Systems Biology, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)

Mentioned by

news
1 news outlet
twitter
1 tweeter
wikipedia
1 Wikipedia page

Citations

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9 Dimensions

Readers on

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47 Mendeley
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Title
The prediction of virus mutation using neural networks and rough set techniques
Published in
EURASIP Journal on Bioinformatics & Systems Biology, May 2016
DOI 10.1186/s13637-016-0042-0
Pubmed ID
Authors

Mostafa A. Salama, Aboul Ella Hassanien, Ahmad Mostafa

Abstract

Viral evolution remains to be a main obstacle in the effectiveness of antiviral treatments. The ability to predict this evolution will help in the early detection of drug-resistant strains and will potentially facilitate the design of more efficient antiviral treatments. Various tools has been utilized in genome studies to achieve this goal. One of these tools is machine learning, which facilitates the study of structure-activity relationships, secondary and tertiary structure evolution prediction, and sequence error correction. This work proposes a novel machine learning technique for the prediction of the possible point mutations that appear on alignments of primary RNA sequence structure. It predicts the genotype of each nucleotide in the RNA sequence, and proves that a nucleotide in an RNA sequence changes based on the other nucleotides in the sequence. Neural networks technique is utilized in order to predict new strains, then a rough set theory based algorithm is introduced to extract these point mutation patterns. This algorithm is applied on a number of aligned RNA isolates time-series species of the Newcastle virus. Two different data sets from two sources are used in the validation of these techniques. The results show that the accuracy of this technique in predicting the nucleotides in the new generation is as high as 75 %. The mutation rules are visualized for the analysis of the correlation between different nucleotides in the same RNA sequence.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 30%
Student > Bachelor 10 21%
Student > Master 8 17%
Student > Postgraduate 3 6%
Student > Doctoral Student 3 6%
Other 6 13%
Unknown 3 6%
Readers by discipline Count As %
Computer Science 10 21%
Engineering 7 15%
Agricultural and Biological Sciences 7 15%
Biochemistry, Genetics and Molecular Biology 5 11%
Medicine and Dentistry 5 11%
Other 9 19%
Unknown 4 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 30 May 2020.
All research outputs
#1,560,354
of 15,743,528 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 52 outputs
Outputs of similar age
#35,099
of 268,880 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 1 outputs
Altmetric has tracked 15,743,528 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 52 research outputs from this source. They receive a mean Attention Score of 1.7. This one has done particularly well, scoring higher than 98% 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 268,880 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 86% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them