↓ Skip to main content

A computational framework for predicting obesity risk based on optimizing and integrating genetic risk score and gene expression profiles

Overview of attention for article published in PLOS ONE, May 2018
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users

Citations

dimensions_citation
14 Dimensions

Readers on

mendeley
37 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A computational framework for predicting obesity risk based on optimizing and integrating genetic risk score and gene expression profiles
Published in
PLOS ONE, May 2018
DOI 10.1371/journal.pone.0197843
Pubmed ID
Authors

Paule V. Joseph, Yupeng Wang, Nicolaas H. Fourie, Wendy A. Henderson

Abstract

Recent large-scale genome-wide association studies have identified tens of genetic loci robustly associated with Body Mass Index (BMI). Gene expression profiles were also found to be associated with BMI. However, accurate prediction of obesity risk utilizing genetic data remains challenging. In a cohort of 75 individuals, we integrated 27 BMI-associated SNPs and obesity-associated gene expression profiles. Genetic risk score was computed by adding BMI-increasing alleles. The genetic risk score was significantly correlated with BMI when an optimization algorithm was used that excluded some SNPs. Linear regression and support vector machine models were built to predict obesity risk using gene expression profiles and the genetic risk score. An adjusted R2 of 0.556 and accuracy of 76% was achieved for the linear regression and support vector machine models, respectively. In this paper, we report a new mathematical method to predict obesity genetic risk. We constructed obesity prediction models based on genetic information for a small cohort. Our computational framework serves as an example for using genetic information to predict obesity risk for specific cohorts.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 27%
Student > Ph. D. Student 8 22%
Student > Bachelor 5 14%
Student > Doctoral Student 2 5%
Professor 1 3%
Other 4 11%
Unknown 7 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 27%
Medicine and Dentistry 7 19%
Computer Science 4 11%
Nursing and Health Professions 2 5%
Sports and Recreations 2 5%
Other 3 8%
Unknown 9 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 June 2018.
All research outputs
#14,222,096
of 23,577,761 outputs
Outputs from PLOS ONE
#118,462
of 202,084 outputs
Outputs of similar age
#178,379
of 331,533 outputs
Outputs of similar age from PLOS ONE
#1,822
of 3,311 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 202,084 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one is in the 40th percentile – i.e., 40% 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 331,533 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3,311 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.