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Mendeley readers
Attention Score in Context
| Title |
MaTSE: the gene expression time-series explorer
|
|---|---|
| Published in |
BMC Bioinformatics, November 2013
|
| DOI | 10.1186/1471-2105-14-s19-s1 |
| Pubmed ID | |
| Authors |
Paul Craig, Alan Cannon, Robert Kukla, Jessie Kennedy |
| Abstract |
High throughput gene expression time-course experiments provide a perspective on biological functioning recognized as having huge value for the diagnosis, treatment, and prevention of diseases. There are however significant challenges to properly exploiting this data due to its massive scale and complexity. In particular, existing techniques are found to be ill suited to finding patterns of changing activity over a limited interval of an experiments time frame. The Time-Series Explorer (TSE) was developed to overcome this limitation by allowing users to explore their data by controlling an animated scatter-plot view. MaTSE improves and extends TSE by allowing users to visualize data with missing values, cross reference multiple conditions, highlight gene groupings, and collaborate by sharing their findings. |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 7% |
| United Kingdom | 1 | 3% |
| Unknown | 27 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 23% |
| Student > Ph. D. Student | 5 | 17% |
| Student > Master | 4 | 13% |
| Student > Bachelor | 3 | 10% |
| Other | 3 | 10% |
| Other | 4 | 13% |
| Unknown | 4 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 30% |
| Agricultural and Biological Sciences | 5 | 17% |
| Engineering | 4 | 13% |
| Biochemistry, Genetics and Molecular Biology | 2 | 7% |
| Arts and Humanities | 2 | 7% |
| Other | 3 | 10% |
| Unknown | 5 | 17% |
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 12 November 2013.
All research outputs
#20,209,145
of 22,729,647 outputs
Outputs from BMC Bioinformatics
#6,838
of 7,266 outputs
Outputs of similar age
#185,011
of 212,425 outputs
Outputs of similar age from BMC Bioinformatics
#105
of 115 outputs
Altmetric has tracked 22,729,647 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 7,266 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 1st percentile – i.e., 1% 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 212,425 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 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.