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A Novel Method to Detect Functional microRNA Regulatory Modules by Bicliques Merging

Overview of attention for article published in IEEE/ACM Transactions on Computational Biology and Bioinformatics, May 2016
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About this Attention Score

  • Among the highest-scoring outputs from this source (#49 of 565)
  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
5 tweeters
peer_reviews
1 peer review site

Citations

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26 Dimensions

Readers on

mendeley
71 Mendeley
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Title
A Novel Method to Detect Functional microRNA Regulatory Modules by Bicliques Merging
Published in
IEEE/ACM Transactions on Computational Biology and Bioinformatics, May 2016
DOI 10.1109/tcbb.2015.2462370
Pubmed ID
Authors

Cheng Liang, Yue Li, Jiawei Luo

Abstract

MicroRNAs (miRNAs) are post-transcriptional regulators that repress the expression of their targets. They are known to work cooperatively with genes and play important roles in numerous cellular processes. Identification of miRNA regulatory modules (MRMs) would aid deciphering the combinatorial effects derived from the many-to-many regulatory relationships in complex cellular systems. Here, we develop an effective method called BiCliques Merging (BCM) to predict MRMs based on bicliques merging. By integrating the miRNA/mRNA expression profiles from The Cancer Genome Atlas (TCGA) with the computational target predictions, we construct a weighted miRNA regulatory network for module discovery. The maximal bicliques detected in the network are statistically evaluated and filtered accordingly. We then employed a greedy-based strategy to iteratively merge the remaining bicliques according to their overlaps together with edge weights and the gene-gene interactions. Comparing with existing methods on two cancer datasets from TCGA, we showed that the modules identified by our method are more densely connected and functionally enriched. Moreover, our predicted modules are more enriched for miRNA families and the miRNA-mRNA pairs within the modules are more negatively correlated. Finally, several potential prognostic modules are revealed by Kaplan-Meier survival analysis and breast cancer subtype analysis. BCM is implemented in Java and available for download in the supplementary materials, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/ TCBB.2015.2462370.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 71 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 15%
Student > Postgraduate 8 11%
Student > Ph. D. Student 7 10%
Student > Bachelor 7 10%
Researcher 6 8%
Other 17 24%
Unknown 15 21%
Readers by discipline Count As %
Computer Science 14 20%
Agricultural and Biological Sciences 9 13%
Engineering 6 8%
Medicine and Dentistry 5 7%
Social Sciences 5 7%
Other 14 20%
Unknown 18 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 10 February 2017.
All research outputs
#3,337,335
of 12,439,028 outputs
Outputs from IEEE/ACM Transactions on Computational Biology and Bioinformatics
#49
of 565 outputs
Outputs of similar age
#79,563
of 268,276 outputs
Outputs of similar age from IEEE/ACM Transactions on Computational Biology and Bioinformatics
#2
of 14 outputs
Altmetric has tracked 12,439,028 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 565 research outputs from this source. They receive a mean Attention Score of 1.9. This one has done particularly well, scoring higher than 91% 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 268,276 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.