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Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers

Overview of attention for article published in Biomarker Research, March 2017
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Title
Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers
Published in
Biomarker Research, March 2017
DOI 10.1186/s40364-017-0089-4
Pubmed ID
Authors

Miao Lu, Jianhui Zhou, Caitlin Naylor, Beth D. Kirkpatrick, Rashidul Haque, William A. Petri, Jennie Z. Ma

Abstract

Environmental Enteropathy (EE) is a subclinical condition caused by constant fecal-oral contamination and resulting in blunting of intestinal villi and intestinal inflammation. Of primary interest in the clinical research is to evaluate the association between non-invasive EE biomarkers and malnutrition in a cohort of Bangladeshi children. The challenges are that the number of biomarkers/covariates is relatively large, and some of them are highly correlated. Many variable selection methods are available in the literature, but which are most appropriate for EE biomarker selection remains unclear. In this study, different variable selection approaches were applied and the performance of these methods was assessed numerically through simulation studies, assuming the correlations among covariates were similar to those in the Bangladesh cohort. The suggested methods from simulations were applied to the Bangladesh cohort to select the most relevant biomarkers for the growth response, and bootstrapping methods were used to evaluate the consistency of selection results. Through simulation studies, SCAD (Smoothly Clipped Absolute Deviation), Adaptive LASSO (Least Absolute Shrinkage and Selection Operator) and MCP (Minimax Concave Penalty) are the suggested variable selection methods, compared to traditional stepwise regression method. In the Bangladesh data, predictors such as mother weight, height-for-age z-score (HAZ) at week 18, and inflammation markers (Myeloperoxidase (MPO) at week 12 and soluable CD14 at week 18) are informative biomarkers associated with children's growth. Penalized linear regression methods are plausible alternatives to traditional variable selection methods, and the suggested methods are applicable to other biomedical studies. The selected early-stage biomarkers offer a potential explanation for the burden of malnutrition problems in low-income countries, allow early identification of infants at risk, and suggest pathways for intervention. This study was retrospectively registered with ClinicalTrials.gov, number NCT01375647, on June 3, 2011.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 19%
Student > Master 10 16%
Researcher 7 11%
Other 5 8%
Student > Postgraduate 4 6%
Other 13 21%
Unknown 12 19%
Readers by discipline Count As %
Medicine and Dentistry 12 19%
Agricultural and Biological Sciences 9 14%
Computer Science 4 6%
Nursing and Health Professions 3 5%
Immunology and Microbiology 3 5%
Other 17 27%
Unknown 15 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 April 2017.
All research outputs
#14,804,186
of 22,968,808 outputs
Outputs from Biomarker Research
#149
of 317 outputs
Outputs of similar age
#182,532
of 307,859 outputs
Outputs of similar age from Biomarker Research
#6
of 8 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 317 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 52% 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 307,859 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
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 2 of them.