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IBDsite: a Galaxy-interacting, integrative database for supporting inflammatory bowel disease high throughput data analysis

Overview of attention for article published in BMC Bioinformatics, September 2012
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1 X user

Citations

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79 Mendeley
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Title
IBDsite: a Galaxy-interacting, integrative database for supporting inflammatory bowel disease high throughput data analysis
Published in
BMC Bioinformatics, September 2012
DOI 10.1186/1471-2105-13-s14-s5
Pubmed ID
Authors

I Merelli, F Viti, L Milanesi

Abstract

Inflammatory bowel diseases (IBD) refer to a group of inflammatory conditions concerning colon and small intestine, which cause socially uncomfortable symptoms and often are associated with an increased risk of colon cancer. IBD are complex disorders, which rely on genetic susceptibility, environmental factors, deregulation of the immune system, and host relationship with commensal microbiota. The complexity of these pathologies makes difficult to clearly understand the mechanisms of their onset. Therefore, the study of IBD must be faced exploiting an integrated and multilevel approach, ranging from genes, transcripts and proteins to pathways altered in affected tissues, and carefully considering their regulatory mechanisms, which may intervene in the pathology onset. It is also crucial to have a knowledge base about the symbiotic bacteria that are hosted in the human gut. To date, much data exist regarding IBD and human commensal bacteria, but this information is sparse in literature and no free resource provides a homogeneously and rationally integrated view of biomolecular data related to these pathologies.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Germany 1 1%
Switzerland 1 1%
Brazil 1 1%
Unknown 75 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 34%
Student > Ph. D. Student 12 15%
Student > Master 10 13%
Student > Bachelor 8 10%
Professor > Associate Professor 4 5%
Other 14 18%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 27%
Medicine and Dentistry 17 22%
Biochemistry, Genetics and Molecular Biology 9 11%
Computer Science 9 11%
Engineering 5 6%
Other 8 10%
Unknown 10 13%
Attention Score in Context

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 26 October 2012.
All research outputs
#20,171,868
of 22,684,168 outputs
Outputs from BMC Bioinformatics
#6,826
of 7,252 outputs
Outputs of similar age
#150,865
of 169,031 outputs
Outputs of similar age from BMC Bioinformatics
#90
of 96 outputs
Altmetric has tracked 22,684,168 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,252 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.