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Mendeley readers
Attention Score in Context
| Title |
A nucleosomal approach to inferring causal relationships of histone modifications
|
|---|---|
| Published in |
BMC Genomics, January 2014
|
| DOI | 10.1186/1471-2164-15-s1-s7 |
| Pubmed ID | |
| Authors |
Ngoc Tu Le, Tu Bao Ho, Bich Hai Ho, Dang Hung Tran |
| Abstract |
Histone proteins are subject to various posttranslational modifications (PTMs). Elucidating their functional relationships is crucial toward understanding many biological processes. Bayesian network (BN)-based approaches have shown the advantage of revealing causal relationships, rather than simple cooccurrences, of PTMs. Previous works employing BNs to infer causal relationships of PTMs require that all confounders should be included. This assumption, however, is unavoidably violated given the fact that several modifications are often regulated by a common but unobserved factor. An existing non-parametric method can be applied to tackle the problem but the complexity and inflexibility make it impractical. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 7% |
| Unknown | 14 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 53% |
| Student > Ph. D. Student | 3 | 20% |
| Professor > Associate Professor | 2 | 13% |
| Student > Bachelor | 1 | 7% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 6 | 40% |
| Biochemistry, Genetics and Molecular Biology | 2 | 13% |
| Computer Science | 2 | 13% |
| Physics and Astronomy | 1 | 7% |
| Decision Sciences | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 2 | 13% |
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 06 November 2014.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from BMC Genomics
#9,840
of 11,244 outputs
Outputs of similar age
#281,383
of 320,960 outputs
Outputs of similar age from BMC Genomics
#174
of 204 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 204 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.