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Indexing a protein-protein interaction network expedites network alignment

Overview of attention for article published in BMC Bioinformatics, October 2015
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Mentioned by

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4 tweeters

Citations

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8 Dimensions

Readers on

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12 Mendeley
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2 CiteULike
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Title
Indexing a protein-protein interaction network expedites network alignment
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0756-0
Pubmed ID
Authors

Md Mahmudul Hasan, Tamer Kahveci

Abstract

Network query problem aligns a small query network with an arbitrarily large target network. The complexity of this problem grows exponentially with the number of nodes in the query network if confidence in the optimality of result is desired. Scaling this problem to large query and target networks remains to be a challenge. In this article, we develop a novel index structure that dramatically reduces the cost of the network query problem. Our index structure maintains a small set of reference networks where each reference network is a small, carefully chosen subnetwork from the target network. Along with each reference, we also store all of its non-overlapping and statistically significant alignments with the target network. Given a query network, we first align the query with the reference networks. If the alignment with a reference network yields a sufficiently large score, we compute an upper-bound to the alignment score between the query and the target using the alignments of that reference and the target (which is stored in our index). If the upper-bound is large enough, we employ a second round of alignment between the query and the target by respecting the mapping found in the first alignment. Our experiments on protein-protein interaction networks demonstrate that our index achieves a significant speed-up in running time over the state-of-the-art methods such as ColT. The alignment subnetworks obtained by our method are also statistically significant. Finally, we observe that our method finds biologically and statistically significant alignments across multiple species. We developed a reference network based indexing structure that accelerates network query and produces functionally and statistically significant results.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 33%
Student > Ph. D. Student 3 25%
Student > Master 2 17%
Student > Bachelor 1 8%
Professor 1 8%
Other 1 8%
Readers by discipline Count As %
Computer Science 4 33%
Agricultural and Biological Sciences 4 33%
Biochemistry, Genetics and Molecular Biology 2 17%
Medicine and Dentistry 2 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 11 October 2015.
All research outputs
#5,894,697
of 10,444,782 outputs
Outputs from BMC Bioinformatics
#2,764
of 4,169 outputs
Outputs of similar age
#119,477
of 248,101 outputs
Outputs of similar age from BMC Bioinformatics
#98
of 147 outputs
Altmetric has tracked 10,444,782 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,169 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 248,101 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 147 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.