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A novel procedure on next generation sequencing data analysis using text mining algorithm

Overview of attention for article published in BMC Bioinformatics, 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 (81st percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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18 X users

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129 Mendeley
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Title
A novel procedure on next generation sequencing data analysis using text mining algorithm
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1075-9
Pubmed ID
Authors

Weizhong Zhao, James J. Chen, Roger Perkins, Yuping Wang, Zhichao Liu, Huixiao Hong, Weida Tong, Wen Zou

Abstract

Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure. The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data. The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
Armenia 1 <1%
Netherlands 1 <1%
France 1 <1%
Cuba 1 <1%
Spain 1 <1%
Unknown 122 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 23%
Student > Ph. D. Student 22 17%
Student > Doctoral Student 14 11%
Student > Master 13 10%
Student > Bachelor 8 6%
Other 19 15%
Unknown 23 18%
Readers by discipline Count As %
Computer Science 26 20%
Agricultural and Biological Sciences 25 19%
Biochemistry, Genetics and Molecular Biology 24 19%
Medicine and Dentistry 7 5%
Engineering 7 5%
Other 14 11%
Unknown 26 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 07 November 2016.
All research outputs
#3,557,484
of 22,870,727 outputs
Outputs from BMC Bioinformatics
#1,268
of 7,297 outputs
Outputs of similar age
#58,419
of 312,377 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 103 outputs
Altmetric has tracked 22,870,727 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,297 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 82% 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 312,377 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 81% of its contemporaries.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.