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Mendeley readers
Attention Score in Context
| Title |
Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects
|
|---|---|
| Published in |
BMC Bioinformatics, November 2012
|
| DOI | 10.1186/1471-2105-13-300 |
| Pubmed ID | |
| Authors |
Kui Wang, Shu Kay Ng, Geoffrey J McLachlan |
| Abstract |
Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models. |
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 % |
|---|---|---|
| Peru | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 2% |
| Brazil | 1 | 2% |
| South Africa | 1 | 2% |
| Russia | 1 | 2% |
| Japan | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 44 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 28% |
| Researcher | 13 | 26% |
| Professor > Associate Professor | 7 | 14% |
| Student > Master | 5 | 10% |
| Professor | 2 | 4% |
| Other | 7 | 14% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 40% |
| Computer Science | 9 | 18% |
| Mathematics | 6 | 12% |
| Biochemistry, Genetics and Molecular Biology | 4 | 8% |
| Engineering | 4 | 8% |
| Other | 3 | 6% |
| Unknown | 4 | 8% |
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 22 November 2012.
All research outputs
#15,256,044
of 22,685,926 outputs
Outputs from BMC Bioinformatics
#5,361
of 7,253 outputs
Outputs of similar age
#112,615
of 179,003 outputs
Outputs of similar age from BMC Bioinformatics
#71
of 104 outputs
Altmetric has tracked 22,685,926 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,253 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 179,003 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 104 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.