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miRCancerdb: a database for correlation analysis between microRNA and gene expression in cancer

Overview of attention for article published in BMC Research Notes, February 2018
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Title
miRCancerdb: a database for correlation analysis between microRNA and gene expression in cancer
Published in
BMC Research Notes, February 2018
DOI 10.1186/s13104-018-3160-9
Pubmed ID
Authors

Mahmoud Ahmed, Huynh Nguyen, Trang Lai, Deok Ryong Kim

Abstract

microRNAs regulate expression of target genes by specifically binding to their transcripts, subsequently leading to translational inhibition or mRNA degradation. Gene regulation by microRNAs has been implicated in a wide range of physiological and pathological conditions. Here, we leverage the use of public-access data and the available genomic annotations to pre-calculate the correlation of the expression of a large number of microRNAs with gene at the mRNA and protein level in the context of cancers. Expression data of miRNAs, mRNAs and proteins in cancer patients from The Cancer Genome Atlas along with TargetScan miRNAs-target annotations were used to calculate the expression correlations between miRNAs and features (mRNAs/proteins) in a number of cancer studies. We then packed the output of this analysis into a database and made it available through an interactive web application. The miRCancerdb is an easy-to-use database to investigate the microRNAs-dependent regulation of target genes involved in development of cancer.

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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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 21%
Student > Bachelor 4 17%
Student > Ph. D. Student 4 17%
Researcher 3 13%
Student > Doctoral Student 2 8%
Other 3 13%
Unknown 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 25%
Biochemistry, Genetics and Molecular Biology 6 25%
Computer Science 4 17%
Engineering 2 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 0 0%
Unknown 5 21%
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 February 2018.
All research outputs
#18,316,001
of 22,679,690 outputs
Outputs from BMC Research Notes
#3,006
of 4,252 outputs
Outputs of similar age
#326,390
of 436,076 outputs
Outputs of similar age from BMC Research Notes
#73
of 115 outputs
Altmetric has tracked 22,679,690 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,252 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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 436,076 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.