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Important miRs of Pathways in Different Tumor Types

Overview of attention for article published in PLoS Computational Biology, January 2013
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Title
Important miRs of Pathways in Different Tumor Types
Published in
PLoS Computational Biology, January 2013
DOI 10.1371/journal.pcbi.1002883
Pubmed ID
Authors

Stefan Wuchty, Dolores Arjona, Peter O. Bauer

Abstract

We computationally determined miRs that are significantly connected to molecular pathways by utilizing gene expression profiles in different cancer types such as glioblastomas, ovarian and breast cancers. Specifically, we assumed that the knowledge of physical interactions between miRs and genes indicated subsets of important miRs (IM) that significantly contributed to the regression of pathway-specific enrichment scores. Despite the different nature of the considered cancer types, we found strongly overlapping sets of IMs. Furthermore, IMs that were important for many pathways were enriched with literature-curated cancer and differentially expressed miRs. Such sets of IMs also coincided well with clusters of miRs that were experimentally indicated in numerous other cancer types. In particular, we focused on an overlapping set of 99 overall important miRs (OIM) that were found in glioblastomas, ovarian and breast cancers simultaneously. Notably, we observed that interactions between OIMs and leading edge genes of differentially expressed pathways were characterized by considerable changes in their expression correlations. Such gains/losses of miR and gene expression correlation indicated miR/gene pairs that may play a causal role in the underlying cancers.

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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 %
Denmark 2 4%
Hungary 1 2%
Brazil 1 2%
Unknown 49 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 40%
Student > Ph. D. Student 14 26%
Student > Master 4 8%
Student > Bachelor 3 6%
Other 2 4%
Other 6 11%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 64%
Computer Science 6 11%
Medicine and Dentistry 5 9%
Biochemistry, Genetics and Molecular Biology 3 6%
Mathematics 1 2%
Other 1 2%
Unknown 3 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 29 January 2013.
All research outputs
#16,387,242
of 25,870,940 outputs
Outputs from PLoS Computational Biology
#7,040
of 9,061 outputs
Outputs of similar age
#180,952
of 290,527 outputs
Outputs of similar age from PLoS Computational Biology
#96
of 136 outputs
Altmetric has tracked 25,870,940 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,061 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one is in the 19th percentile – i.e., 19% 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 290,527 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 136 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.