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Multitask learning improves prediction of cancer drug sensitivity

Overview of attention for article published in Scientific Reports, August 2016
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Mentioned by

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1 tweeter

Citations

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57 Dimensions

Readers on

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120 Mendeley
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Title
Multitask learning improves prediction of cancer drug sensitivity
Published in
Scientific Reports, August 2016
DOI 10.1038/srep31619
Pubmed ID
Authors

Han Yuan, Ivan Paskov, Hristo Paskov, Alvaro J. González, Christina S. Leslie

Abstract

Precision oncology seeks to predict the best therapeutic option for individual patients based on the molecular characteristics of their tumors. To assess the preclinical feasibility of drug sensitivity prediction, several studies have measured drug responses for cytotoxic and targeted therapies across large collections of genomically and transcriptomically characterized cancer cell lines and trained predictive models using standard methods like elastic net regression. Here we use existing drug response data sets to demonstrate that multitask learning across drugs strongly improves the accuracy and interpretability of drug prediction models. Our method uses trace norm regularization with a highly efficient ADMM (alternating direction method of multipliers) optimization algorithm that readily scales to large data sets. We anticipate that our approach will enhance efforts to exploit growing drug response compendia in order to advance personalized therapy.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Denmark 1 <1%
Unknown 117 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 30%
Researcher 20 17%
Student > Master 16 13%
Student > Bachelor 12 10%
Student > Doctoral Student 6 5%
Other 23 19%
Unknown 7 6%
Readers by discipline Count As %
Computer Science 36 30%
Biochemistry, Genetics and Molecular Biology 23 19%
Agricultural and Biological Sciences 13 11%
Engineering 10 8%
Pharmacology, Toxicology and Pharmaceutical Science 6 5%
Other 21 18%
Unknown 11 9%

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 11 September 2016.
All research outputs
#4,458,875
of 8,360,145 outputs
Outputs from Scientific Reports
#21,472
of 36,870 outputs
Outputs of similar age
#139,152
of 252,646 outputs
Outputs of similar age from Scientific Reports
#1,873
of 3,215 outputs
Altmetric has tracked 8,360,145 research outputs across all sources so far. This one is in the 27th percentile – i.e., 27% of other outputs scored the same or lower than it.
So far Altmetric has tracked 36,870 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one is in the 32nd percentile – i.e., 32% 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 252,646 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3,215 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.