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Exploiting topic modeling to boost metagenomic reads binning

Overview of attention for article published in BMC Bioinformatics, March 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

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12 tweeters

Citations

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12 Dimensions

Readers on

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42 Mendeley
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Title
Exploiting topic modeling to boost metagenomic reads binning
Published in
BMC Bioinformatics, March 2015
DOI 10.1186/1471-2105-16-s5-s2
Pubmed ID
Authors

Ruichang Zhang, Zhanzhan Cheng, Jihong Guan, Shuigeng Zhou

Abstract

With the rapid development of high-throughput technologies, researchers can sequence the whole metagenome of a microbial community sampled directly from the environment. The assignment of these metagenomic reads into different species or taxonomical classes is a vital step for metagenomic analysis, which is referred to as binning of metagenomic data. In this paper, we propose a new method TM-MCluster for binning metagenomic reads. First, we represent each metagenomic read as a set of "k-mers" with their frequencies occurring in the read. Then, we employ a probabilistic topic model -- the Latent Dirichlet Allocation (LDA) model to the reads, which generates a number of hidden "topics" such that each read can be represented by a distribution vector of the generated topics. Finally, as in the MCluster method, we apply SKWIC -- a variant of the classical K-means algorithm with automatic feature weighting mechanism to cluster these reads represented by topic distributions. Experiments show that the new method TM-MCluster outperforms major existing methods, including AbundanceBin, MetaCluster 3.0/5.0 and MCluster. This result indicates that the exploitation of topic modeling can effectively improve the binning performance of metagenomic reads.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Argentina 1 2%
Unknown 40 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 21%
Researcher 7 17%
Student > Bachelor 6 14%
Student > Master 5 12%
Student > Postgraduate 3 7%
Other 6 14%
Unknown 6 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 29%
Computer Science 10 24%
Biochemistry, Genetics and Molecular Biology 4 10%
Mathematics 2 5%
Engineering 2 5%
Other 7 17%
Unknown 5 12%

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 30 November 2017.
All research outputs
#4,516,830
of 22,805,349 outputs
Outputs from BMC Bioinformatics
#1,706
of 7,281 outputs
Outputs of similar age
#61,924
of 285,988 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 141 outputs
Altmetric has tracked 22,805,349 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,281 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 76% 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 285,988 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 78% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.