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Brain cancer prognosis: independent validation of a clinical bioinformatics approach

Overview of attention for article published in Journal of Clinical Bioinformatics, February 2012
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  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

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3 X users

Citations

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2 Dimensions

Readers on

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18 Mendeley
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Title
Brain cancer prognosis: independent validation of a clinical bioinformatics approach
Published in
Journal of Clinical Bioinformatics, February 2012
DOI 10.1186/2043-9113-2-2
Pubmed ID
Authors

Raffaele Fronza, Michele Tramonti, William R Atchley, Christine Nardini

Abstract

Translational and evidence based medicine can take advantage of biotechnology advances that offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. The clinical information hidden in these data can be clarified with clinical bioinformatics approaches. We have recently proposed a method to analyze different layers of high-throughput (omic) data to preserve the emergent properties that appear in the cellular system when all molecular levels are interacting. We show here that this method applied to brain cancer data can uncover properties (i.e. molecules related to protective versus risky features in different types of brain cancers) that have been independently validated as survival markers, with potential important application in clinical practice.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 6%
Italy 1 6%
Unknown 16 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 33%
Researcher 6 33%
Other 2 11%
Student > Bachelor 2 11%
Professor 1 6%
Other 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 44%
Medicine and Dentistry 3 17%
Biochemistry, Genetics and Molecular Biology 2 11%
Computer Science 2 11%
Business, Management and Accounting 1 6%
Other 2 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 28 March 2019.
All research outputs
#16,046,765
of 25,371,288 outputs
Outputs from Journal of Clinical Bioinformatics
#26
of 61 outputs
Outputs of similar age
#163,288
of 253,515 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#4
of 9 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one has gotten more attention than average, scoring higher than 52% 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 253,515 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.