↓ Skip to main content

Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer

Overview of attention for article published in BMC Medical Genomics, May 2017
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
25 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
Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer
Published in
BMC Medical Genomics, May 2017
DOI 10.1186/s12920-017-0268-z
Pubmed ID
Authors

Garam Lee, Lisa Bang, So Yeon Kim, Dokyoon Kim, Kyung-Ah Sohn

Abstract

Breast cancer is a complex disease in which different genomic patterns exists depending on different subtypes. Recent researches present that multiple subtypes of breast cancer occur at different rates, and play a crucial role in planning treatment. To better understand underlying biological mechanisms on breast cancer subtypes, investigating the specific gene regulatory system via different subtypes is desirable. Gene expression, as an intermediate phenotype, is estimated based on methylation profiles to identify the impact of epigenomic features on transcriptomic changes in breast cancer. We propose a kernel weighted l1-regularized regression model to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes. For the proper control of subtype-specific estimation, samples from different breast cancer subtype are learned at different rate based on target estimates. Kolmogorov Smirnov test is conducted to determine learning rate of each sample from different subtype. It is observed that genes that might be sensitive to breast cancer subtype show prediction improvement when estimated using our proposed method. Comparing to a standard method, overall performance is also enhanced by incorporating tumor subtypes. In addition, we identified subtype-specific network structures based on the associations between gene expression and DNA methylation. In this study, kernel weighted lasso model is proposed for identifying subtype-specific associations between gene expressions and DNA methylation profiles. Identification of subtype-specific gene expression associated with epigenomic changes might be helpful for better planning treatment and developing new therapies.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 24%
Researcher 3 12%
Student > Master 3 12%
Professor > Associate Professor 2 8%
Student > Bachelor 2 8%
Other 4 16%
Unknown 5 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 32%
Agricultural and Biological Sciences 4 16%
Computer Science 3 12%
Nursing and Health Professions 1 4%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 7 28%
Attention Score in Context

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 08 June 2017.
All research outputs
#20,427,593
of 22,979,862 outputs
Outputs from BMC Medical Genomics
#1,010
of 1,229 outputs
Outputs of similar age
#272,961
of 313,678 outputs
Outputs of similar age from BMC Medical Genomics
#15
of 17 outputs
Altmetric has tracked 22,979,862 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,229 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 1st percentile – i.e., 1% 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 313,678 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.