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Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

Overview of attention for article published in BMC Cancer, June 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

dimensions_citation
69 Dimensions

Readers on

mendeley
129 Mendeley
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Title
Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection
Published in
BMC Cancer, June 2015
DOI 10.1186/s12885-015-1492-6
Pubmed ID
Authors

Zuoli Dong, Naiqian Zhang, Chun Li, Haiyun Wang, Yun Fang, Jun Wang, Xiaoqi Zheng

Abstract

An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel. Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP). Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80 % accuracy for 10 drugs, ≥ 75 % accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively. These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

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

Geographical breakdown

Country Count As %
Spain 1 <1%
Hong Kong 1 <1%
United States 1 <1%
China 1 <1%
Unknown 125 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 29%
Student > Master 24 19%
Researcher 19 15%
Student > Bachelor 7 5%
Professor > Associate Professor 5 4%
Other 19 15%
Unknown 17 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 30 23%
Computer Science 28 22%
Agricultural and Biological Sciences 23 18%
Engineering 7 5%
Medicine and Dentistry 5 4%
Other 13 10%
Unknown 23 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 13 February 2017.
All research outputs
#2,503,975
of 15,807,984 outputs
Outputs from BMC Cancer
#595
of 5,894 outputs
Outputs of similar age
#40,986
of 234,225 outputs
Outputs of similar age from BMC Cancer
#1
of 1 outputs
Altmetric has tracked 15,807,984 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,894 research outputs from this source. They receive a mean Attention Score of 4.1. This one has done well, scoring higher than 89% 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 234,225 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them