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Non-coding RNAs in Complex Diseases

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Attention for Chapter 10: Computational Identification of Cross-Talking ceRNAs
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Chapter title
Computational Identification of Cross-Talking ceRNAs
Chapter number 10
Book title
Non-coding RNAs in Complex Diseases
Published in
Advances in experimental medicine and biology, September 2018
DOI 10.1007/978-981-13-0719-5_10
Pubmed ID
Book ISBNs
978-9-81-130718-8, 978-9-81-130719-5
Authors

Yongsheng Li, Caiqin Huo, Xiaoyu Lin, Juan Xu, Li, Yongsheng, Huo, Caiqin, Lin, Xiaoyu, Xu, Juan

Abstract

Competing endogenous RNAs (ceRNAs) are kinds of RNAs that regulate each other at post-transcription level through competing for miRNA regulators. CeRNA-ceRNA networks provide another type of function for protein-coding mRNAs, which link non-coding RNAs such as miRNA, long non-coding RNA, pseudogenes and circular RNAs. In this chapter, we will introduce the definition of ceRNAs, mainly provide the computational method to predict ceRNA interactions in general condition and complex diseases. In addition, we also illustrated several computational methods that are commonly used to identify the perturbed ceRNA networks in human diseases compared to normal conditions. Finally, we also summarized the principles of methods that integrated ceRNA theory to identify human disease biomarkers. Understanding of RNA-RNA crosstalk will provide significant insights into gene regulatory network that has been implicated in human development and/or diseases.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 33%
Unknown 2 67%
Readers by discipline Count As %
Unspecified 1 33%
Unknown 2 67%
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 08 September 2018.
All research outputs
#18,649,291
of 23,103,436 outputs
Outputs from Advances in experimental medicine and biology
#3,337
of 4,976 outputs
Outputs of similar age
#258,125
of 336,158 outputs
Outputs of similar age from Advances in experimental medicine and biology
#63
of 81 outputs
Altmetric has tracked 23,103,436 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 4,976 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. 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 336,158 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 81 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.