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Pathway Analysis of the Human Brain Transcriptome in Disease

Overview of attention for article published in Journal of Molecular Neuroscience, December 2012
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1 CiteULike
Title
Pathway Analysis of the Human Brain Transcriptome in Disease
Published in
Journal of Molecular Neuroscience, December 2012
DOI 10.1007/s12031-012-9940-0
Pubmed ID
Authors

Tomas Kavanagh, James D. Mills, Woojin S. Kim, Glenda M. Halliday, Michael Janitz

Abstract

Pathway analysis is a powerful method for discerning differentially regulated genes and elucidating their biological importance. It allows for the identification of perturbed or aberrantly expressed genes within a biological context from extensive data sets and offers a simplistic approach for interrogating such data sets. With the growing use of microarrays and RNA-Seq, data for genome-wide studies are growing at an alarming rate, and the use of deep sequencing is revealing elements of the genome previously uncharacterised. Through the employment of pathway analysis, mechanisms in complex diseases may be explored and novel causatives found primarily through differentially regulated genes. Further, with the implementation of next generation sequencing, a deeper resolution may be attained, particularly in identification of isoform diversity and SNPs. Here, we look at a broad overview of pathway analysis in the human brain transcriptome and its relevance in teasing out underlying causes of complex diseases. We will outline processes in data gathering and analysis of particular diseases in which these approaches have been successful.

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X Demographics

The data shown below were collected from the profiles of 2 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Luxembourg 1 2%
Brazil 1 2%
Unknown 51 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 28%
Researcher 13 25%
Student > Master 6 11%
Student > Bachelor 5 9%
Student > Postgraduate 4 8%
Other 6 11%
Unknown 4 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 43%
Biochemistry, Genetics and Molecular Biology 6 11%
Neuroscience 5 9%
Medicine and Dentistry 5 9%
Psychology 3 6%
Other 7 13%
Unknown 4 8%
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 07 February 2013.
All research outputs
#17,286,645
of 25,374,917 outputs
Outputs from Journal of Molecular Neuroscience
#972
of 1,643 outputs
Outputs of similar age
#193,719
of 288,988 outputs
Outputs of similar age from Journal of Molecular Neuroscience
#7
of 11 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,643 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.