| Title |
TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach
|
|---|---|
| Published in |
BMC Bioinformatics, March 2010
|
| DOI | 10.1186/1471-2105-11-154 |
| Pubmed ID | |
| Authors |
Pietro Zoppoli, Sandro Morganella, Michele Ceccarelli |
| Abstract |
One of main aims of Molecular Biology is the gain of knowledge about how molecular components interact each other and to understand gene function regulations. Using microarray technology, it is possible to extract measurements of thousands of genes into a single analysis step having a picture of the cell gene expression. Several methods have been developed to infer gene networks from steady-state data, much less literature is produced about time-course data, so the development of algorithms to infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 33% |
| United States | 1 | 33% |
| Italy | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 267 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 3% |
| United Kingdom | 7 | 3% |
| Germany | 5 | 2% |
| Sweden | 3 | 1% |
| Italy | 1 | <1% |
| New Caledonia | 1 | <1% |
| Switzerland | 1 | <1% |
| India | 1 | <1% |
| Chile | 1 | <1% |
| Other | 5 | 2% |
| Unknown | 233 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 78 | 29% |
| Researcher | 74 | 28% |
| Student > Master | 25 | 9% |
| Professor > Associate Professor | 18 | 7% |
| Professor | 13 | 5% |
| Other | 47 | 18% |
| Unknown | 12 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 117 | 44% |
| Computer Science | 50 | 19% |
| Biochemistry, Genetics and Molecular Biology | 42 | 16% |
| Engineering | 10 | 4% |
| Physics and Astronomy | 8 | 3% |
| Other | 24 | 9% |
| Unknown | 16 | 6% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 September 2019.
All research outputs
#13,672,464
of 22,681,577 outputs
Outputs from BMC Bioinformatics
#4,435
of 7,250 outputs
Outputs of similar age
#74,479
of 94,446 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 61 outputs
Altmetric has tracked 22,681,577 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,250 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 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.