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Sparse generalized linear model with L0 approximation for feature selection and prediction with big omics data

Overview of attention for article published in BioData Mining, December 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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1 X user
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4 patents
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2 Q&A threads

Citations

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13 Dimensions

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19 Mendeley
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Title
Sparse generalized linear model with L0 approximation for feature selection and prediction with big omics data
Published in
BioData Mining, December 2017
DOI 10.1186/s13040-017-0159-z
Pubmed ID
Authors

Zhenqiu Liu, Fengzhu Sun, Dermot P. McGovern

Abstract

Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L1, SCAD and MC+. However, none of the existing algorithms optimizes L0, which penalizes the number of nonzero features directly. In this paper, we develop a novel sparse generalized linear model (GLM) with L0 approximation for feature selection and prediction with big omics data. The proposed approach approximate the L0 optimization directly. Even though the original L0 problem is non-convex, the problem is approximated by sequential convex optimizations with the proposed algorithm. The proposed method is easy to implement with only several lines of code. Novel adaptive ridge algorithms (L0ADRIDGE) for L0 penalized GLM with ultra high dimensional big data are developed. The proposed approach outperforms the other cutting edge regularization methods including SCAD and MC+ in simulations. When it is applied to integrated analysis of mRNA, microRNA, and methylation data from TCGA ovarian cancer, multilevel gene signatures associated with suboptimal debulking are identified simultaneously. The biological significance and potential clinical importance of those genes are further explored. The developed Software L0ADRIDGE in MATLAB is available at https://github.com/liuzqx/L0adridge.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 26%
Student > Bachelor 2 11%
Other 1 5%
Lecturer 1 5%
Student > Master 1 5%
Other 1 5%
Unknown 8 42%
Readers by discipline Count As %
Engineering 2 11%
Economics, Econometrics and Finance 2 11%
Mathematics 1 5%
Nursing and Health Professions 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 3 16%
Unknown 9 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 29 November 2023.
All research outputs
#2,950,328
of 24,950,117 outputs
Outputs from BioData Mining
#56
of 320 outputs
Outputs of similar age
#63,178
of 452,295 outputs
Outputs of similar age from BioData Mining
#4
of 8 outputs
Altmetric has tracked 24,950,117 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 320 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one has done well, scoring higher than 82% 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 452,295 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 85% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.