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Measuring semantic similarities by combining gene ontology annotations and gene co-function networks

Overview of attention for article published in BMC Bioinformatics, February 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)

Mentioned by

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11 tweeters

Citations

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39 Dimensions

Readers on

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54 Mendeley
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Title
Measuring semantic similarities by combining gene ontology annotations and gene co-function networks
Published in
BMC Bioinformatics, February 2015
DOI 10.1186/s12859-015-0474-7
Pubmed ID
Authors

Jiajie Peng, Sahra Uygun, Taehyong Kim, Yadong Wang, Seung Y Rhee, Jin Chen

Abstract

Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited. Supplementary information and software are available at http://www.msu.edu/~jinchen/NETSIM .

Twitter Demographics

The data shown below were collected from the profiles of 11 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 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Brazil 1 2%
Unknown 52 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 22%
Researcher 8 15%
Student > Bachelor 7 13%
Student > Master 6 11%
Student > Doctoral Student 5 9%
Other 8 15%
Unknown 8 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 37%
Computer Science 12 22%
Biochemistry, Genetics and Molecular Biology 8 15%
Engineering 3 6%
Medicine and Dentistry 2 4%
Other 1 2%
Unknown 8 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 26 August 2015.
All research outputs
#5,077,462
of 19,211,930 outputs
Outputs from BMC Bioinformatics
#2,025
of 6,554 outputs
Outputs of similar age
#59,365
of 228,838 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 19,211,930 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 6,554 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 68% 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 228,838 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them