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Inferring interaction type in gene regulatory networks using co-expression data

Overview of attention for article published in Algorithms for Molecular Biology, July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#36 of 264)
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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8 X users
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1 patent

Citations

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29 Dimensions

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64 Mendeley
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1 CiteULike
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Title
Inferring interaction type in gene regulatory networks using co-expression data
Published in
Algorithms for Molecular Biology, July 2015
DOI 10.1186/s13015-015-0054-4
Pubmed ID
Authors

Pegah Khosravi, Vahid H Gazestani, Leila Pirhaji, Brian Law, Mehdi Sadeghi, Bahram Goliaei, Gary D Bader

Abstract

Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction. This paper describes a novel algorithm, "Signing of Regulatory Networks" (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN. SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Germany 1 2%
Canada 1 2%
Unknown 60 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 25%
Researcher 15 23%
Student > Master 7 11%
Student > Bachelor 5 8%
Professor > Associate Professor 4 6%
Other 8 13%
Unknown 9 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 33%
Biochemistry, Genetics and Molecular Biology 18 28%
Computer Science 10 16%
Immunology and Microbiology 2 3%
Chemical Engineering 1 2%
Other 2 3%
Unknown 10 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 02 March 2017.
All research outputs
#4,430,663
of 22,816,807 outputs
Outputs from Algorithms for Molecular Biology
#36
of 264 outputs
Outputs of similar age
#55,690
of 262,361 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 8 outputs
Altmetric has tracked 22,816,807 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done well, scoring higher than 86% 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 262,361 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 78% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.