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Reverse engineering gene regulatory networks: Coupling an optimization algorithm with a parameter identification technique

Overview of attention for article published in BMC Bioinformatics, December 2014
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Title
Reverse engineering gene regulatory networks: Coupling an optimization algorithm with a parameter identification technique
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/1471-2105-15-s15-s8
Pubmed ID
Authors

Yu-Ting Hsiao, Wei-Po Lee

Abstract

To infer gene regulatory networks from time series gene profiles, two important tasks that are related to biological systems must be undertaken. One task is to determine a valid network structure that has topological properties that can influence the network dynamics profoundly. The other task is to optimize the network parameters to minimize the accumulated discrepancy between the gene expression data and the values produced by the inferred network model. Though the above two tasks must be conducted simultaneously, most existing work addresses only one of the tasks.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 4%
Taiwan 1 4%
Unknown 22 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 33%
Researcher 5 21%
Student > Bachelor 2 8%
Professor > Associate Professor 2 8%
Student > Master 1 4%
Other 3 13%
Unknown 3 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 33%
Computer Science 7 29%
Business, Management and Accounting 2 8%
Biochemistry, Genetics and Molecular Biology 2 8%
Engineering 2 8%
Other 0 0%
Unknown 3 13%
Attention Score in Context

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 05 December 2014.
All research outputs
#17,733,724
of 22,772,779 outputs
Outputs from BMC Bioinformatics
#5,927
of 7,276 outputs
Outputs of similar age
#247,386
of 360,895 outputs
Outputs of similar age from BMC Bioinformatics
#119
of 147 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 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 13th percentile – i.e., 13% 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 360,895 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 147 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.