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Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration

Overview of attention for article published in BMC Genomics, September 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

blogs
1 blog
twitter
14 X users

Citations

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52 Dimensions

Readers on

mendeley
84 Mendeley
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2 CiteULike
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Title
Mergeomics: a web server for identifying pathological pathways, networks, and key regulators via multidimensional data integration
Published in
BMC Genomics, September 2016
DOI 10.1186/s12864-016-3057-8
Pubmed ID
Authors

Douglas Arneson, Anindya Bhattacharya, Le Shu, Ville-Petteri Mäkinen, Xia Yang

Abstract

Human diseases are commonly the result of multidimensional changes at molecular, cellular, and systemic levels. Recent advances in genomic technologies have enabled an outpour of omics datasets that capture these changes. However, separate analyses of these various data only provide fragmented understanding and do not capture the holistic view of disease mechanisms. To meet the urgent needs for tools that effectively integrate multiple types of omics data to derive biological insights, we have developed Mergeomics, a computational pipeline that integrates multidimensional disease association data with functional genomics and molecular networks to retrieve biological pathways, gene networks, and central regulators critical for disease development. To make the Mergeomics pipeline available to a wider research community, we have implemented an online, user-friendly web server ( http://mergeomics. idre.ucla.edu/ ). The web server features a modular implementation of the Mergeomics pipeline with detailed tutorials. Additionally, it provides curated genomic resources including tissue-specific expression quantitative trait loci, ENCODE functional annotations, biological pathways, and molecular networks, and offers interactive visualization of analytical results. Multiple computational tools including Marker Dependency Filtering (MDF), Marker Set Enrichment Analysis (MSEA), Meta-MSEA, and Weighted Key Driver Analysis (wKDA) can be used separately or in flexible combinations. User-defined summary-level genomic association datasets (e.g., genetic, transcriptomic, epigenomic) related to a particular disease or phenotype can be uploaded and computed real-time to yield biologically interpretable results, which can be viewed online and downloaded for later use. Our Mergeomics web server offers researchers flexible and user-friendly tools to facilitate integration of multidimensional data into holistic views of disease mechanisms in the form of tissue-specific key regulators, biological pathways, and gene networks.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Finland 1 1%
Switzerland 1 1%
Unknown 81 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 26 31%
Student > Ph. D. Student 16 19%
Other 7 8%
Student > Master 7 8%
Student > Bachelor 6 7%
Other 11 13%
Unknown 11 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 39%
Agricultural and Biological Sciences 19 23%
Medicine and Dentistry 7 8%
Immunology and Microbiology 4 5%
Computer Science 3 4%
Other 8 10%
Unknown 10 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 20 November 2018.
All research outputs
#2,515,238
of 23,881,329 outputs
Outputs from BMC Genomics
#771
of 10,793 outputs
Outputs of similar age
#43,847
of 333,165 outputs
Outputs of similar age from BMC Genomics
#16
of 298 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,793 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 92% 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 333,165 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 86% of its contemporaries.
We're also able to compare this research output to 298 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.