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Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge

Overview of attention for article published in BMC Bioinformatics, March 2012
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Title
Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge
Published in
BMC Bioinformatics, March 2012
DOI 10.1186/1471-2105-13-46
Pubmed ID
Authors

Chia-Ling Huang, John Lamb, Leonid Chindelevitch, Jarek Kostrowicki, Justin Guinney, Charles DeLisi, Daniel Ziemek

Abstract

Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators. We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between regulators and their regulatees, we focus on coherence of regulatees of a regulator. Using simulated datasets we show that our method performs very well at recovering even weak regulatory relationships with a low false discovery rate. Using three separate real biological datasets we were able to recover well known and as yet undescribed, active regulators for each disease population. The results are represented as a rank-ordered list of regulators, and reveals both single and higher-order regulatory relationships. CSA is an intuitive data-driven way of selecting directed perturbation experiments that are relevant to a disease population of interest and represent a starting point for further investigation. Our findings demonstrate that combining co-expression analysis on regulatee sets with a literature-derived network can successfully identify causal regulators and help develop possible hypothesis to explain disease progression.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 8%
Germany 1 2%
Switzerland 1 2%
Italy 1 2%
Netherlands 1 2%
Slovenia 1 2%
Sweden 1 2%
Unknown 43 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 28%
Student > Ph. D. Student 11 21%
Professor 7 13%
Professor > Associate Professor 5 9%
Student > Master 5 9%
Other 7 13%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 43%
Computer Science 12 23%
Biochemistry, Genetics and Molecular Biology 5 9%
Engineering 4 8%
Mathematics 2 4%
Other 4 8%
Unknown 3 6%
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 23 March 2012.
All research outputs
#15,242,707
of 22,663,969 outputs
Outputs from BMC Bioinformatics
#5,357
of 7,247 outputs
Outputs of similar age
#102,351
of 160,528 outputs
Outputs of similar age from BMC Bioinformatics
#52
of 72 outputs
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