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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 (#22 of 177)
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
9 tweeters
patent
1 patent

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
58 Mendeley
citeulike
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.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
United States 1 2%
United Kingdom 1 2%
Canada 1 2%
Unknown 53 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 26%
Researcher 15 26%
Student > Bachelor 5 9%
Student > Master 4 7%
Professor > Associate Professor 4 7%
Other 8 14%
Unknown 7 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 36%
Biochemistry, Genetics and Molecular Biology 16 28%
Computer Science 9 16%
Chemical Engineering 1 2%
Immunology and Microbiology 1 2%
Other 2 3%
Unknown 8 14%

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
#1,742,433
of 11,293,566 outputs
Outputs from Algorithms for Molecular Biology
#22
of 177 outputs
Outputs of similar age
#45,773
of 232,751 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 6 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 177 research outputs from this source. They receive a mean Attention Score of 2.8. This one has done well, scoring higher than 87% 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 232,751 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 80% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.