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Sparse canonical methods for biological data integration: application to a cross-platform study

Overview of attention for article published in BMC Bioinformatics, January 2009
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Mentioned by

q&a
1 Q&A thread

Citations

dimensions_citation
161 Dimensions

Readers on

mendeley
270 Mendeley
citeulike
9 CiteULike
connotea
1 Connotea
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Title
Sparse canonical methods for biological data integration: application to a cross-platform study
Published in
BMC Bioinformatics, January 2009
DOI 10.1186/1471-2105-10-34
Pubmed ID
Authors

Kim-Anh Lê Cao, Pascal GP Martin, Christèle Robert-Granié, Philippe Besse

Abstract

In the context of systems biology, few sparse approaches have been proposed so far to integrate several data sets. It is however an important and fundamental issue that will be widely encountered in post genomic studies, when simultaneously analyzing transcriptomics, proteomics and metabolomics data using different platforms, so as to understand the mutual interactions between the different data sets. In this high dimensional setting, variable selection is crucial to give interpretable results. We focus on a sparse Partial Least Squares approach (sPLS) to handle two-block data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been developed either for a regression or a canonical correlation framework and includes a built-in procedure to select variables while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines.

Mendeley readers

The data shown below were compiled from readership statistics for 270 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 4%
Germany 4 1%
United Kingdom 4 1%
Belgium 3 1%
Netherlands 2 <1%
Portugal 2 <1%
Sweden 1 <1%
Australia 1 <1%
Italy 1 <1%
Other 6 2%
Unknown 236 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 84 31%
Student > Ph. D. Student 71 26%
Professor > Associate Professor 22 8%
Other 14 5%
Student > Master 13 5%
Other 47 17%
Unknown 19 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 94 35%
Computer Science 35 13%
Biochemistry, Genetics and Molecular Biology 35 13%
Mathematics 31 11%
Medicine and Dentistry 13 5%
Other 36 13%
Unknown 26 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 09 June 2011.
All research outputs
#6,432,481
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,249
of 4,576 outputs
Outputs of similar age
#46,563
of 86,517 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 34 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 48th percentile – i.e., 48% 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 86,517 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.