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Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification

Overview of attention for article published in BMC Bioinformatics, June 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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2 X users
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1 patent
q&a
1 Q&A thread

Citations

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123 Dimensions

Readers on

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75 Mendeley
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2 CiteULike
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Title
Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification
Published in
BMC Bioinformatics, June 2013
DOI 10.1186/1471-2105-14-198
Pubmed ID
Authors

Yong Liang, Cheng Liu, Xin-Ze Luan, Kwong-Sak Leung, Tak-Ming Chan, Zong-Ben Xu, Hai Zhang

Abstract

Microarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regularization approach is one of the effective methods for gene selection in microarray data, which generally contain a large number of genes and have a small number of samples. In recent years, various approaches have been developed for gene selection of microarray data. Generally, they are divided into three categories: filter, wrapper and embedded methods. Regularization methods are an important embedded technique and perform both continuous shrinkage and automatic gene selection simultaneously. Recently, there is growing interest in applying the regularization techniques in gene selection. The popular regularization technique is Lasso (L1), and many L1 type regularization terms have been proposed in the recent years. Theoretically, the Lq type regularization with the lower value of q would lead to better solutions with more sparsity. Moreover, the L1/2 regularization can be taken as a representative of Lq (0 <q < 1) regularizations and has been demonstrated many attractive properties.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Malaysia 1 1%
Netherlands 1 1%
Denmark 1 1%
Unknown 70 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 21%
Student > Ph. D. Student 13 17%
Student > Master 12 16%
Student > Postgraduate 6 8%
Student > Bachelor 4 5%
Other 14 19%
Unknown 10 13%
Readers by discipline Count As %
Computer Science 16 21%
Mathematics 14 19%
Agricultural and Biological Sciences 9 12%
Biochemistry, Genetics and Molecular Biology 9 12%
Engineering 6 8%
Other 6 8%
Unknown 15 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 24 December 2019.
All research outputs
#4,582,239
of 22,712,476 outputs
Outputs from BMC Bioinformatics
#1,760
of 7,259 outputs
Outputs of similar age
#39,745
of 196,823 outputs
Outputs of similar age from BMC Bioinformatics
#26
of 89 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 75% 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 196,823 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 79% of its contemporaries.
We're also able to compare this research output to 89 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 70% of its contemporaries.