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Co-acting gene networks predict TRAIL responsiveness of tumour cells with high accuracy

Overview of attention for article published in BMC Genomics, January 2014
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Title
Co-acting gene networks predict TRAIL responsiveness of tumour cells with high accuracy
Published in
BMC Genomics, January 2014
DOI 10.1186/1471-2164-15-1144
Pubmed ID
Authors

Paul O’Reilly, Csaba Ortutay, Grainne Gernon, Enda O’Connell, Cathal Seoighe, Susan Boyce, Luis Serrano, Eva Szegezdi

Abstract

Identification of differentially expressed genes from transcriptomic studies is one of the most common mechanisms to identify tumor biomarkers. This approach however is not well suited to identify interaction between genes whose protein products potentially influence each other, which limits its power to identify molecular wiring of tumour cells dictating response to a drug. Due to the fact that signal transduction pathways are not linear and highly interlinked, the biological response they drive may be better described by the relative amount of their components and their functional relationships than by their individual, absolute expression.

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

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 32%
Student > Bachelor 3 14%
Student > Ph. D. Student 3 14%
Unspecified 2 9%
Student > Postgraduate 2 9%
Other 5 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 27%
Agricultural and Biological Sciences 4 18%
Unspecified 3 14%
Engineering 3 14%
Immunology and Microbiology 1 5%
Other 5 23%

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 15 August 2015.
All research outputs
#3,859,380
of 5,484,117 outputs
Outputs from BMC Genomics
#3,885
of 4,857 outputs
Outputs of similar age
#128,528
of 191,899 outputs
Outputs of similar age from BMC Genomics
#235
of 294 outputs
Altmetric has tracked 5,484,117 research outputs across all sources so far. This one is in the 16th percentile – i.e., 16% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,857 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 10th percentile – i.e., 10% 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 191,899 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 294 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.