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Inferring plant microRNA functional similarity using a weighted protein-protein interaction network

Overview of attention for article published in BMC Bioinformatics, November 2015
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5 tweeters

Citations

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8 Dimensions

Readers on

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19 Mendeley
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Title
Inferring plant microRNA functional similarity using a weighted protein-protein interaction network
Published in
BMC Bioinformatics, November 2015
DOI 10.1186/s12859-015-0789-4
Pubmed ID
Authors

Jun Meng, Dong Liu, Yushi Luan

Abstract

MiRNAs play a critical role in the response of plants to abiotic and biotic stress. However, the functions of most plant miRNAs remain unknown. Inferring these functions from miRNA functional similarity would thus be useful. This study proposes a new method, called PPImiRFS, for inferring miRNA functional similarity. The functional similarity of miRNAs was inferred from the functional similarity of their target gene sets. A protein-protein interaction network with semantic similarity weights of edges generated using Gene Ontology terms was constructed to infer the functional similarity between two target genes that belong to two different miRNAs, and the score for functional similarity was calculated using the weighted shortest path for the two target genes through the whole network. The experimental results showed that the proposed method was more effective and reliable than previous methods (miRFunSim and GOSemSim) applied to Arabidopsis thaliana. Additionally, miRNAs responding to the same type of stress had higher functional similarity than miRNAs responding to different types of stress. For the first time, a protein-protein interaction network with semantic similarity weights generated using Gene Ontology terms was employed to calculate the functional similarity of plant miRNAs. A novel method based on calculating the weighted shortest path between two target genes was introduced.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 4 21%
Researcher 4 21%
Student > Bachelor 4 21%
Professor 3 16%
Student > Ph. D. Student 2 11%
Other 2 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 32%
Biochemistry, Genetics and Molecular Biology 5 26%
Computer Science 4 21%
Engineering 2 11%
Unspecified 1 5%
Other 0 0%
Unknown 1 5%

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 06 November 2015.
All research outputs
#7,431,444
of 12,378,687 outputs
Outputs from BMC Bioinformatics
#2,967
of 4,543 outputs
Outputs of similar age
#127,397
of 265,795 outputs
Outputs of similar age from BMC Bioinformatics
#99
of 153 outputs
Altmetric has tracked 12,378,687 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,543 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 265,795 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 153 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.