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NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis

Overview of attention for article published in BMC Systems Biology, September 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

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2 Dimensions

Readers on

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31 Mendeley
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Title
NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis
Published in
BMC Systems Biology, September 2017
DOI 10.1186/s12918-017-0456-7
Pubmed ID
Authors

Duanchen Sun, Yinliang Liu, Xiang-Sun Zhang, Ling-Yun Wu

Abstract

High-throughput experimental techniques have been dramatically improved and widely applied in the past decades. However, biological interpretation of the high-throughput experimental results, such as differential expression gene sets derived from microarray or RNA-seq experiments, is still a challenging task. Gene Ontology (GO) is commonly used in the functional enrichment studies. The GO terms identified via current functional enrichment analysis tools often contain direct parent or descendant terms in the GO hierarchical structure. Highly redundant terms make users difficult to analyze the underlying biological processes. In this paper, a novel network-based probabilistic generative model, NetGen, was proposed to perform the functional enrichment analysis. An additional protein-protein interaction (PPI) network was explicitly used to assist the identification of significantly enriched GO terms. NetGen achieved a superior performance than the existing methods in the simulation studies. The effectiveness of NetGen was explored further on four real datasets. Notably, several GO terms which were not directly linked with the active gene list for each disease were identified. These terms were closely related to the corresponding diseases when accessed to the curated literatures. NetGen has been implemented in the R package CopTea publicly available at GitHub ( http://github.com/wulingyun/CopTea/ ). Our procedure leads to a more reasonable and interpretable result of the functional enrichment analysis. As a novel term combination-based functional enrichment analysis method, NetGen is complementary to current individual term-based methods, and can help to explore the underlying pathogenesis of complex diseases.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 26%
Student > Master 5 16%
Student > Bachelor 5 16%
Researcher 5 16%
Professor > Associate Professor 2 6%
Other 2 6%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 35%
Biochemistry, Genetics and Molecular Biology 9 29%
Computer Science 3 10%
Engineering 2 6%
Neuroscience 1 3%
Other 1 3%
Unknown 4 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 28 September 2017.
All research outputs
#1,729,603
of 11,841,124 outputs
Outputs from BMC Systems Biology
#90
of 993 outputs
Outputs of similar age
#58,810
of 270,536 outputs
Outputs of similar age from BMC Systems Biology
#2
of 14 outputs
Altmetric has tracked 11,841,124 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 993 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 90% 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 270,536 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 78% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.