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Towards precision medicine-based therapies for glioblastoma: interrogating human disease genomics and mouse phenotypes

Overview of attention for article published in BMC Genomics, August 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
18 Mendeley
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Title
Towards precision medicine-based therapies for glioblastoma: interrogating human disease genomics and mouse phenotypes
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2908-7
Pubmed ID
Authors

Yang Chen, Zhen Gao, Bingcheng Wang, Rong Xu

Abstract

Glioblastoma (GBM) is the most common and aggressive brain tumors. It has poor prognosis even with optimal radio- and chemo-therapies. Since GBM is highly heterogeneous, drugs that target on specific molecular profiles of individual tumors may achieve maximized efficacy. Currently, the Cancer Genome Atlas (TCGA) projects have identified hundreds of GBM-associated genes. We develop a drug repositioning approach combining disease genomics and mouse phenotype data towards predicting targeted therapies for GBM. We first identified disease specific mouse phenotypes using the most recently discovered GBM genes. Then we systematically searched all FDA-approved drugs for candidates that share similar mouse phenotype profiles with GBM. We evaluated the ranks for approved and novel GBM drugs, and compared with an existing approach, which also use the mouse phenotype data but not the disease genomics data. We achieved significantly higher ranks for the approved and novel GBM drugs than the earlier approach. For all positive examples of GBM drugs, we achieved a median rank of 9.2 45.6 of the top predictions have been demonstrated effective in inhibiting the growth of human GBM cells. We developed a computational drug repositioning approach based on both genomic and phenotypic data. Our approach prioritized existing GBM drugs and outperformed a recent approach. Overall, our approach shows potential in discovering new targeted therapies for GBM.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 %
Student > Ph. D. Student 6 33%
Researcher 4 22%
Student > Bachelor 3 17%
Student > Postgraduate 3 17%
Other 1 6%
Other 1 6%
Readers by discipline Count As %
Medicine and Dentistry 6 33%
Computer Science 2 11%
Biochemistry, Genetics and Molecular Biology 2 11%
Agricultural and Biological Sciences 2 11%
Neuroscience 2 11%
Other 4 22%

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 27 August 2016.
All research outputs
#6,172,077
of 11,464,969 outputs
Outputs from BMC Genomics
#3,214
of 6,874 outputs
Outputs of similar age
#108,154
of 259,187 outputs
Outputs of similar age from BMC Genomics
#109
of 273 outputs
Altmetric has tracked 11,464,969 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,874 research outputs from this source. They receive a mean Attention Score of 4.2. This one has gotten more attention than average, scoring higher than 50% 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 259,187 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 273 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.