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A systematic comparison of genome-scale clustering algorithms

Overview of attention for article published in BMC Bioinformatics, January 2012
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1 tweeter

Citations

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50 Mendeley
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Title
A systematic comparison of genome-scale clustering algorithms
Published in
BMC Bioinformatics, January 2012
DOI 10.1186/1471-2105-13-s10-s7
Pubmed ID
Authors

Jeremy J Jay, John D Eblen, Yun Zhang, Mikael Benson, Andy D Perkins, Arnold M Saxton, Brynn H Voy, Elissa J Chesler, Michael A Langston

Abstract

A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae.

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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
Cuba 1 2%
Unknown 47 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 36%
Student > Ph. D. Student 11 22%
Student > Master 7 14%
Student > Doctoral Student 3 6%
Other 3 6%
Other 6 12%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 38%
Biochemistry, Genetics and Molecular Biology 9 18%
Computer Science 9 18%
Engineering 3 6%
Neuroscience 2 4%
Other 5 10%
Unknown 3 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 10 February 2015.
All research outputs
#2,520,191
of 4,741,736 outputs
Outputs from BMC Bioinformatics
#1,930
of 2,774 outputs
Outputs of similar age
#89,870
of 169,349 outputs
Outputs of similar age from BMC Bioinformatics
#102
of 138 outputs
Altmetric has tracked 4,741,736 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,774 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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 169,349 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.