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Identification of large disjoint motifs in biological networks

Overview of attention for article published in BMC Bioinformatics, October 2016
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2 tweeters

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15 Mendeley
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Title
Identification of large disjoint motifs in biological networks
Published in
BMC Bioinformatics, October 2016
DOI 10.1186/s12859-016-1271-7
Pubmed ID
Authors

Rasha Elhesha, Tamer Kahveci

Abstract

Biological networks provide great potential to understand how cells function. Network motifs, frequent topological patterns, are key structures through which biological networks operate. Finding motifs in biological networks remains to be computationally challenging task as the size of the motif and the underlying network grow. Often, different copies of a given motif topology in a network share nodes or edges. Counting such overlapping copies introduces significant problems in motif identification. In this paper, we develop a scalable algorithm for finding network motifs. Unlike most of the existing studies, our algorithm counts independent copies of each motif topology. We introduce a set of small patterns and prove that we can construct any larger pattern by joining those patterns iteratively. By iteratively joining already identified motifs with those patterns, our algorithm avoids (i) constructing topologies which do not exist in the target network (ii) repeatedly counting the frequency of the motifs generated in subsequent iterations. Our experiments on real and synthetic networks demonstrate that our method is significantly faster and more accurate than the existing methods including SUBDUE and FSG. We conclude that our method for finding network motifs is scalable and computationally feasible for large motif sizes and a broad range of networks with different sizes and densities. We proved that any motif with four or more edges can be constructed as a join of the small patterns.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Singapore 1 7%
Unknown 14 93%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 3 20%
Researcher 3 20%
Student > Ph. D. Student 3 20%
Professor 2 13%
Student > Bachelor 2 13%
Other 1 7%
Unknown 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 40%
Biochemistry, Genetics and Molecular Biology 4 27%
Computer Science 1 7%
Engineering 1 7%
Unknown 3 20%

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 15 October 2016.
All research outputs
#8,687,824
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#3,310
of 4,195 outputs
Outputs of similar age
#166,756
of 257,713 outputs
Outputs of similar age from BMC Bioinformatics
#96
of 137 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.