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Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

Overview of attention for article published in Biology Direct, December 2012
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1 X user

Citations

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23 Dimensions

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56 Mendeley
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Title
Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods
Published in
Biology Direct, December 2012
DOI 10.1186/1745-6150-7-44
Pubmed ID
Authors

Frank Emmert-Streib, Shailesh Tripathi, Ricardo de Matos Simoes

Abstract

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Brazil 1 2%
Sweden 1 2%
India 1 2%
Spain 1 2%
Unknown 51 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 39%
Student > Ph. D. Student 12 21%
Student > Bachelor 4 7%
Student > Postgraduate 4 7%
Professor > Associate Professor 3 5%
Other 6 11%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 45%
Biochemistry, Genetics and Molecular Biology 8 14%
Medicine and Dentistry 6 11%
Computer Science 5 9%
Mathematics 2 4%
Other 5 9%
Unknown 5 9%
Attention Score in Context

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 11 December 2012.
All research outputs
#20,655,488
of 25,371,288 outputs
Outputs from Biology Direct
#446
of 537 outputs
Outputs of similar age
#226,704
of 286,419 outputs
Outputs of similar age from Biology Direct
#13
of 13 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 537 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.