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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Post-Transcriptional Gene Regulation
|
|---|---|
| Chapter number | 2 |
| Book title |
Post-Transcriptional Gene Regulation
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3067-8_2 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3066-1, 978-1-4939-3067-8
|
| Authors |
Marchese, Domenica, Livi, Carmen Maria, Tartaglia, Gian Gaetano, Domenica Marchese, Carmen Maria Livi, Gian Gaetano Tartaglia |
| Editors |
Erik Dassi |
| Abstract |
Protein-RNA interactions play important roles in a wide variety of cellular processes, ranging from transcriptional and posttranscriptional regulation of genes to host defense against pathogens. In this chapter we present the computational approach catRAPID to predict protein-RNA interactions and discuss how it could be used to find trends in ribonucleoprotein networks. We envisage that the combination of computational and experimental approaches will be crucial to unravel the role of coding and noncoding RNAs in protein networks. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 9% |
| Unknown | 10 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 45% |
| Professor > Associate Professor | 2 | 18% |
| Researcher | 2 | 18% |
| Student > Doctoral Student | 1 | 9% |
| Unknown | 1 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 64% |
| Neuroscience | 2 | 18% |
| Biochemistry, Genetics and Molecular Biology | 1 | 9% |
| Unknown | 1 | 9% |
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 09 July 2016.
All research outputs
#17,810,867
of 22,880,230 outputs
Outputs from Methods in molecular biology
#7,245
of 13,132 outputs
Outputs of similar age
#267,805
of 393,712 outputs
Outputs of similar age from Methods in molecular biology
#752
of 1,471 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,132 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 39th percentile – i.e., 39% 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 393,712 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,471 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.