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Efficient test for nonlinear dependence of two continuous variables

Overview of attention for article published in BMC Bioinformatics, August 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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7 X users

Citations

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40 Dimensions

Readers on

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104 Mendeley
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Title
Efficient test for nonlinear dependence of two continuous variables
Published in
BMC Bioinformatics, August 2015
DOI 10.1186/s12859-015-0697-7
Pubmed ID
Authors

Yi Wang, Yi Li, Hongbao Cao, Momiao Xiong, Yin Yao Shugart, Li Jin

Abstract

Testing dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work, we proposed a new way of testing nonlinear dependence between two continuous variables (X and Y). We addressed this research question by using CANOVA (continuous analysis of variance, software available at https://sourceforge.net/projects/canova/ ). In the CANOVA framework, we first defined a neighborhood for each data point related to its X value, and then calculated the variance of the Y value within the neighborhood. Finally, we performed permutations to evaluate the significance of the observed values within the neighborhood variance. To evaluate the strength of CANOVA compared to six other methods, we performed extensive simulations to explore the relationship between methods and compared the false positive rates and statistical power using both simulated and real datasets (kidney cancer RNA-seq dataset). We concluded that CANOVA is an efficient method for testing nonlinear correlation with several advantages in real data applications.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 <1%
Korea, Republic of 1 <1%
Brazil 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 99 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 24%
Researcher 12 12%
Student > Master 10 10%
Student > Doctoral Student 8 8%
Student > Bachelor 7 7%
Other 15 14%
Unknown 27 26%
Readers by discipline Count As %
Computer Science 16 15%
Agricultural and Biological Sciences 10 10%
Biochemistry, Genetics and Molecular Biology 9 9%
Engineering 9 9%
Neuroscience 5 5%
Other 24 23%
Unknown 31 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 January 2023.
All research outputs
#6,517,108
of 24,162,843 outputs
Outputs from BMC Bioinformatics
#2,350
of 7,506 outputs
Outputs of similar age
#72,046
of 270,486 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 122 outputs
Altmetric has tracked 24,162,843 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,506 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 68% 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 270,486 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 73% of its contemporaries.
We're also able to compare this research output to 122 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 68% of its contemporaries.