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Prediction of epigenetically regulated genes in breast cancer cell lines

Overview of attention for article published in BMC Bioinformatics, June 2010
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
googleplus
1 Google+ user

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
74 Mendeley
citeulike
3 CiteULike
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Title
Prediction of epigenetically regulated genes in breast cancer cell lines
Published in
BMC Bioinformatics, June 2010
DOI 10.1186/1471-2105-11-305
Pubmed ID
Authors

Leandro A Loss, Anguraj Sadanandam, Steffen Durinck, Shivani Nautiyal, Diane Flaucher, Victoria EH Carlton, Martin Moorhead, Yontao Lu, Joe W Gray, Malek Faham, Paul Spellman, Bahram Parvin

Abstract

Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profiles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profiles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fixed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis.

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
United Kingdom 3 4%
Canada 2 3%
France 1 1%
Italy 1 1%
Turkey 1 1%
Belgium 1 1%
Singapore 1 1%
Unknown 60 81%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 27%
Researcher 16 22%
Student > Master 11 15%
Professor > Associate Professor 6 8%
Other 6 8%
Other 11 15%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 42%
Biochemistry, Genetics and Molecular Biology 10 14%
Medicine and Dentistry 10 14%
Computer Science 8 11%
Mathematics 2 3%
Other 9 12%
Unknown 4 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 January 2014.
All research outputs
#2,925,422
of 22,707,247 outputs
Outputs from BMC Bioinformatics
#1,044
of 7,255 outputs
Outputs of similar age
#11,394
of 95,945 outputs
Outputs of similar age from BMC Bioinformatics
#8
of 72 outputs
Altmetric has tracked 22,707,247 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,255 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 85% 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 95,945 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 87% of its contemporaries.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.