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Mendeley readers
Attention Score in Context
| Title |
Graph reconstruction using covariance-based methods
|
|---|---|
| Published in |
EURASIP Journal on Bioinformatics & Systems Biology, November 2016
|
| DOI | 10.1186/s13637-016-0052-y |
| Pubmed ID | |
| Authors |
Nurgazy Sulaimanov, Heinz Koeppl |
| Abstract |
Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the case when the number of variables exceeds the number of samples. In this study, we investigate how the graphs extracted from covariance and concentration matrix estimates are related by using Neumann series and transitive closure and through discussing concrete small examples. Considering the ideal case where the true graph is available, we also compare correlation and partial correlation methods for large realistic graphs. In particular, we perform the comparisons with optimally selected parameters based on the true underlying graph and with data-driven approaches where the parameters are directly estimated from the data. |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 33% |
| Researcher | 6 | 17% |
| Student > Bachelor | 4 | 11% |
| Professor | 3 | 8% |
| Other | 3 | 8% |
| Other | 4 | 11% |
| Unknown | 4 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 8 | 22% |
| Biochemistry, Genetics and Molecular Biology | 6 | 17% |
| Computer Science | 5 | 14% |
| Mathematics | 4 | 11% |
| Neuroscience | 3 | 8% |
| Other | 3 | 8% |
| Unknown | 7 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 18 January 2017.
All research outputs
#6,755,702
of 25,394,764 outputs
Outputs from EURASIP Journal on Bioinformatics & Systems Biology
#8
of 53 outputs
Outputs of similar age
#111,409
of 415,360 outputs
Outputs of similar age from EURASIP Journal on Bioinformatics & Systems Biology
#1
of 2 outputs
Altmetric has tracked 25,394,764 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 53 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done well, scoring higher than 84% 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 415,360 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 73% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them