↓ Skip to main content

Modeling precision treatment of breast cancer.

Overview of attention for article published in Genome Biology (Online Edition), October 2013
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Readers on

mendeley
194 Mendeley
citeulike
7 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Modeling precision treatment of breast cancer.
Published in
Genome Biology (Online Edition), October 2013
DOI 10.1186/gb-2013-14-10-r110
Pubmed ID
Authors

Daemen A, Griffith OL, Heiser LM, Wang NJ, Enache OM, Sanborn Z, Pepin F, Durinck S, Korkola JE, Griffith M, Hur JS, Huh N, Chung J, Cope L, Fackler MJ, Umbricht C, Sukumar S, Seth P, Sukhatme VP, Jakkula LR, Lu Y, Mills GB, Cho RJ, Collisson EA, Van't Veer LJ, Spellman PT, Gray JW, Anneleen Daemen, Obi L Griffith, Laura M Heiser, Nicholas J Wang, Oana M Enache, Zachary Sanborn, Francois Pepin, Steffen Durinck, James E Korkola, Malachi Griffith, Joe S Hur, Nam Huh, Jongsuk Chung, Leslie Cope, Mary Fackler, Christopher Umbricht, Saraswati Sukumar, Pankaj Seth, Vikas P Sukhatme, Lakshmi R Jakkula, Yiling Lu, Gordon B Mills, Raymond J Cho, Eric A Collisson, Laura J van’t Veer, Paul T Spellman, Joe W Gray

Abstract

First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets.

Twitter Demographics

The data shown below were collected from the profiles of 23 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 194 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 6%
United Kingdom 5 3%
France 2 1%
Italy 2 1%
Denmark 2 1%
Spain 2 1%
Germany 1 <1%
Australia 1 <1%
Mexico 1 <1%
Other 4 2%
Unknown 162 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 65 34%
Student > Ph. D. Student 55 28%
Student > Master 20 10%
Student > Bachelor 11 6%
Professor > Associate Professor 11 6%
Other 32 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 98 51%
Biochemistry, Genetics and Molecular Biology 29 15%
Computer Science 24 12%
Medicine and Dentistry 22 11%
Mathematics 6 3%
Other 15 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 February 2015.
All research outputs
#352,812
of 8,002,013 outputs
Outputs from Genome Biology (Online Edition)
#451
of 2,314 outputs
Outputs of similar age
#7,497
of 145,747 outputs
Outputs of similar age from Genome Biology (Online Edition)
#12
of 58 outputs
Altmetric has tracked 8,002,013 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,314 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.2. This one has done well, scoring higher than 80% 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 145,747 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.