↓ Skip to main content

Identification of gene pairs through penalized regression subject to constraints

Overview of attention for article published in BMC Bioinformatics, November 2017
Altmetric Badge

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
20 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
Identification of gene pairs through penalized regression subject to constraints
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1872-9
Pubmed ID
Authors

Rex Shen, Lan Luo, Hui Jiang

Abstract

This article concerns the identification of gene pairs or combinations of gene pairs associated with biological phenotype or clinical outcome, allowing for building predictive models that are not only robust to normalization but also easily validated and measured by qPCR techniques. However, given a small number of biological samples yet a large number of genes, this problem suffers from the difficulty of high computational complexity and imposes challenges to the accuracy of identification statistically. In this paper, we propose a parsimonious model representation and develop efficient algorithms for identification. Particularly, we derive an equivalent model subject to a sum-to-zero constraint in penalized linear regression, where the correspondence between nonzero coefficients in these models is established. Most importantly, it reduces the model complexity of the traditional approach from the quadratic order to the linear order in the number of candidate genes, while overcoming the difficulty of model nonidentifiablity. Computationally, we develop an algorithm using the alternating direction method of multipliers (ADMM) to deal with the constraint. Numerically, we demonstrate that the proposed method outperforms the traditional method in terms of the statistical accuracy. Moreover, we demonstrate that our ADMM algorithm is more computationally efficient than a coordinate descent algorithm with a local search. Finally, we illustrate the proposed method on a prostate cancer dataset to identify gene pairs that are associated with pre-operative prostate-specific antigen. Our findings demonstrate the feasibility and utility of using gene pairs as biomarkers.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 25%
Student > Master 4 20%
Professor > Associate Professor 3 15%
Student > Bachelor 3 15%
Student > Doctoral Student 1 5%
Other 2 10%
Unknown 2 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 30%
Engineering 2 10%
Computer Science 2 10%
Nursing and Health Professions 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 3 15%
Unknown 5 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 03 November 2017.
All research outputs
#9,669,634
of 12,091,568 outputs
Outputs from BMC Bioinformatics
#3,662
of 4,401 outputs
Outputs of similar age
#206,528
of 284,599 outputs
Outputs of similar age from BMC Bioinformatics
#147
of 194 outputs
Altmetric has tracked 12,091,568 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,401 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 8th percentile – i.e., 8% 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 284,599 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 194 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.