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The LO-BaFL method and ALS microarray expression analysis

Overview of attention for article published in BMC Bioinformatics, January 2012
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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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

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6 Dimensions

Readers on

mendeley
18 Mendeley
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Title
The LO-BaFL method and ALS microarray expression analysis
Published in
BMC Bioinformatics, January 2012
DOI 10.1186/1471-2105-13-244
Pubmed ID
Authors

Cristina Baciu, Kevin J Thompson, Jean-Luc Mougeot, Benjamin R Brooks, Jennifer W Weller

Abstract

Sporadic Amyotrophic Lateral Sclerosis (sALS) is a devastating, complex disease of unknown etiology. We studied this disease with microarray technology to capture as much biological complexity as possible. The Affymetrix-focused BaFL pipeline takes into account problems with probes that arise from physical and biological properties, so we adapted it to handle the long-oligonucleotide probes on our arrays (hence LO-BaFL). The revised method was tested against a validated array experiment and then used in a meta-analysis of peripheral white blood cells from healthy control samples in two experiments. We predicted differentially expressed (DE) genes in our sALS data, combining the results obtained using the TM4 suite of tools with those from the LO-BaFL method. Those predictions were tested using qRT-PCR assays.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 39%
Student > Bachelor 5 28%
Student > Ph. D. Student 2 11%
Student > Master 1 6%
Professor > Associate Professor 1 6%
Other 1 6%
Unknown 1 6%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 33%
Agricultural and Biological Sciences 5 28%
Engineering 2 11%
Computer Science 1 6%
Neuroscience 1 6%
Other 2 11%
Unknown 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 24 September 2012.
All research outputs
#1,799,861
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#780
of 4,588 outputs
Outputs of similar age
#18,140
of 127,930 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 38 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,588 research outputs from this source. They receive a mean Attention Score of 4.9. 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 127,930 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 85% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.